Song Popularity Prediction using Machine Learning

BDA502: Introduction to Machine Learning Term Project

Kutay Akalın
15/05/2020

Libraries

In [15]:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import json
import statsmodels.api as sm
import collections
import seaborn as sn
import missingno as msno
from itables import show 

from sklearn.decomposition import PCA
from sklearn.cluster import KMeans,DBSCAN, AgglomerativeClustering
from sklearn.feature_selection import RFE, SelectKBest, chi2, SelectFromModel
from sklearn.preprocessing import MinMaxScaler, RobustScaler, StandardScaler
from sklearn.model_selection import train_test_split, RandomizedSearchCV, GridSearchCV
from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier, IsolationForest

from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve, precision_score, recall_score, f1_score, r2_score 
from sklearn.metrics import precision_recall_curve, auc, average_precision_score, plot_precision_recall_curve,confusion_matrix

from sklearn.model_selection import cross_val_score, cross_validate
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier

Dataset

In the BDA 502 term project, I anlaysed features of the tracks and tried to understand correlation between features and popularity. For this purpose, I obtained 80.000 Techno tracks and their features using Spotify Web API. Basically, Spotify presents various endpoints for retrieving information in the API. Artists, Albums, Playlists, Users Profile are some of the examples of these endpoints. More information can be found in references. For this project, mainly 3 types of information were obtained:

  • Audio Feature Information
  • Track Catalog Information
  • Artist Catalog Information

In this project, my main purpose is predicting binary popularity feature using supervised machine learning methods and selecting the best algorithm for popularity prediction.

In [2]:
with open('D:\\Int. ML\\Dönem Ödevi\\track_data.json', encoding="utf8", errors='ignore') as f:
  data = json.load(f)
df = pd.DataFrame(data)

display(df.describe())
display(df.info())
display(df.head())
Track_Popularity danceability energy key loudness mode speechiness acousticness instrumentalness liveness valence tempo time_signature disc_number Duration(ms) Album_Number Artist_Popularity Artist_Followers
count 84234.000000 84234.000000 84234.000000 84234.000000 84234.000000 84234.000000 84234.000000 84234.000000 84234.000000 84234.000000 84234.000000 84234.00000 84234.000000 84234.000000 8.423400e+04 84234.000000 84234.000000 84234.000000
mean 5.582200 0.678843 0.699812 5.677316 -10.554455 0.554728 0.078187 0.109724 0.773937 0.160672 0.286353 126.34931 3.922264 1.006434 3.802934e+05 4.584408 30.732839 27283.797920
std 8.732592 0.165013 0.208948 3.674555 4.177622 0.496999 0.070853 0.225262 0.244552 0.148046 0.230919 16.09560 0.417253 0.085135 2.156867e+05 3.902907 15.202694 60910.246215
min 0.000000 0.000000 0.000000 0.000000 -60.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 1.000000 2.413000e+03 1.000000 6.000000 4.000000
25% 0.000000 0.615000 0.572000 2.000000 -12.177000 0.000000 0.044600 0.000800 0.782000 0.090200 0.086200 123.71250 4.000000 1.000000 3.155585e+05 2.000000 17.000000 898.000000
50% 1.000000 0.723000 0.731000 6.000000 -9.878000 1.000000 0.056800 0.008000 0.870000 0.109000 0.226000 127.00000 4.000000 1.000000 3.779075e+05 3.000000 31.000000 5763.000000
75% 8.000000 0.797000 0.867000 9.000000 -8.010000 1.000000 0.081100 0.078300 0.909000 0.144000 0.434000 130.00000 4.000000 1.000000 4.305140e+05 6.000000 42.000000 26368.000000
max 65.000000 0.994000 1.000000 11.000000 1.464000 1.000000 0.967000 0.996000 0.999000 0.990000 1.000000 249.99300 5.000000 4.000000 5.638440e+06 20.000000 69.000000 766930.000000
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 84234 entries, 0 to 84233
Data columns (total 31 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   id                     84234 non-null  object 
 1   Track_Name             84234 non-null  object 
 2   Track_Popularity       84234 non-null  int64  
 3   danceability           84234 non-null  float64
 4   energy                 84234 non-null  float64
 5   key                    84234 non-null  int64  
 6   loudness               84234 non-null  float64
 7   mode                   84234 non-null  int64  
 8   speechiness            84234 non-null  float64
 9   acousticness           84234 non-null  float64
 10  instrumentalness       84234 non-null  float64
 11  liveness               84234 non-null  float64
 12  valence                84234 non-null  float64
 13  tempo                  84234 non-null  float64
 14  track_href             84234 non-null  object 
 15  analysis_url           84234 non-null  object 
 16  time_signature         84234 non-null  int64  
 17  artists                84234 non-null  object 
 18  available_markets      84234 non-null  object 
 19  disc_number            84234 non-null  int64  
 20  Duration(ms)           84234 non-null  int64  
 21  explicit               84234 non-null  bool   
 22  is_local               84234 non-null  bool   
 23  preview_url            82895 non-null  object 
 24  Album_Number           84234 non-null  int64  
 25  external_urls.spotify  84234 non-null  object 
 26  Artist_ID              84234 non-null  object 
 27  Artist_Name            84234 non-null  object 
 28  Artist_Popularity      84234 non-null  int64  
 29  Artist_Followers       84234 non-null  int64  
 30  Artist_Genres          84234 non-null  object 
dtypes: bool(2), float64(9), int64(9), object(11)
memory usage: 18.8+ MB
None
id Track_Name Track_Popularity danceability energy key loudness mode speechiness acousticness ... explicit is_local preview_url Album_Number external_urls.spotify Artist_ID Artist_Name Artist_Popularity Artist_Followers Artist_Genres
0 0OUU3JfuXy4bU18ROT5AED Capsule 38 0.800 0.722 1 -7.854 0 0.0861 0.0004 ... False False https://p.scdn.co/mp3-preview/23392f5c081dcb96... 1 https://open.spotify.com/track/0OUU3JfuXy4bU18... 1btv9qmIpbp7q1ixCYNdHu Adam Beyer 58 200007 [dark techno, minimal techno, raw techno, schr...
1 60uf1maGeSPYxevT53w534 In Love 33 0.774 0.686 8 -6.763 1 0.0670 0.0001 ... False False https://p.scdn.co/mp3-preview/b31921e259b6d361... 2 https://open.spotify.com/track/60uf1maGeSPYxev... 1btv9qmIpbp7q1ixCYNdHu Adam Beyer 58 200007 [dark techno, minimal techno, raw techno, schr...
2 4TRsYNLesIsKcsY8fUEnvi Atmosfear 28 0.800 0.863 1 -7.198 1 0.0569 0.0016 ... False False https://p.scdn.co/mp3-preview/6462b25fcc03e0bc... 3 https://open.spotify.com/track/4TRsYNLesIsKcsY... 1btv9qmIpbp7q1ixCYNdHu Adam Beyer 58 200007 [dark techno, minimal techno, raw techno, schr...
3 17UV3ByF3BqbH6LZIqs33p We Are E 30 0.803 0.645 7 -8.949 1 0.0808 0.0015 ... False False https://p.scdn.co/mp3-preview/de4da46f65721d72... 4 https://open.spotify.com/track/17UV3ByF3BqbH6L... 1btv9qmIpbp7q1ixCYNdHu Adam Beyer 58 200007 [dark techno, minimal techno, raw techno, schr...
4 4IBVGAqCMjQX5jAnJkGMi9 Ignition Key 20 0.537 0.941 5 -7.788 0 0.0320 0.0000 ... False False https://p.scdn.co/mp3-preview/578a28c2979390c0... 1 https://open.spotify.com/track/4IBVGAqCMjQX5jA... 1btv9qmIpbp7q1ixCYNdHu Adam Beyer 58 200007 [dark techno, minimal techno, raw techno, schr...

5 rows × 31 columns

Missing Values

In [3]:
msno.matrix(df)
Out[3]:
<matplotlib.axes._subplots.AxesSubplot at 0x1ec245e2be0>
In [4]:
df.isna().sum()
Out[4]:
id                          0
Track_Name                  0
Track_Popularity            0
danceability                0
energy                      0
key                         0
loudness                    0
mode                        0
speechiness                 0
acousticness                0
instrumentalness            0
liveness                    0
valence                     0
tempo                       0
track_href                  0
analysis_url                0
time_signature              0
artists                     0
available_markets           0
disc_number                 0
Duration(ms)                0
explicit                    0
is_local                    0
preview_url              1339
Album_Number                0
external_urls.spotify       0
Artist_ID                   0
Artist_Name                 0
Artist_Popularity           0
Artist_Followers            0
Artist_Genres               0
dtype: int64

Data Cleaning and Preprocessing

In [5]:
# Selecting meaningfull columns for prediction
slicing = list(range(2, 14)) + [16,20,24,28,29]
tracks=df.iloc[:,slicing]

# Cleaning the tracks which have 0 popularity and 0 tempo (assumed to be missing data)
tracks = tracks[np.logical_and(tracks.Track_Popularity > 0,tracks.tempo >0)].reset_index(drop = True)
tracks
Out[5]:
Track_Popularity danceability energy key loudness mode speechiness acousticness instrumentalness liveness valence tempo time_signature Duration(ms) Album_Number Artist_Popularity Artist_Followers
0 38 0.800 0.722 1 -7.854 0 0.0861 0.0004 0.902 0.1010 0.505 127.003 4 416692 1 58 200007
1 33 0.774 0.686 8 -6.763 1 0.0670 0.0001 0.882 0.0902 0.068 126.026 4 541428 2 58 200007
2 28 0.800 0.863 1 -7.198 1 0.0569 0.0016 0.900 0.0773 0.155 124.007 4 408115 3 58 200007
3 30 0.803 0.645 7 -8.949 1 0.0808 0.0015 0.847 0.0996 0.409 123.991 4 482419 4 58 200007
4 20 0.537 0.941 5 -7.788 0 0.0320 0.0000 0.873 0.0709 0.420 132.000 4 484747 1 58 200007
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
50780 1 0.623 0.876 5 -6.846 1 0.0364 0.0001 0.889 0.1460 0.424 123.994 4 484400 2 11 638
50781 17 0.687 0.737 4 -6.577 0 0.0910 0.0154 0.876 0.1110 0.525 134.998 4 441128 1 14 1322
50782 13 0.602 0.825 6 -4.665 1 0.1520 0.0094 0.898 0.0859 0.457 128.002 4 369843 2 14 1322
50783 12 0.727 0.788 1 -6.680 1 0.0713 0.0063 0.797 0.1090 0.237 126.019 4 353280 3 14 1322
50784 12 0.802 0.632 3 -8.077 0 0.1020 0.0028 0.854 0.1140 0.143 130.025 4 366240 4 14 1322

50785 rows × 17 columns

Binning Track Popularity

To solve this prediction problem with classification approach, I splitted the Track Puplarity feature into two binary categories (popular, not popular) by using median because popularity column is highly skewed. Thus, we can increase the accuracy of the machine learning models.

In [6]:
sns.set(style="darkgrid")
plt.figure(figsize=(16, 8))
sns.kdeplot(tracks.Track_Popularity,shade = True, linewidth = 5)
Out[6]:
<matplotlib.axes._subplots.AxesSubplot at 0x1ec24b40b70>
In [7]:
#Binning Track Popularity using median
ftracks = tracks.copy()
ftracks["Popular"] = [1 if ftracks.loc[i,"Track_Popularity"] > np.median(ftracks.Track_Popularity) else 0 for i in ftracks.index]
display(ftracks)
Track_Popularity danceability energy key loudness mode speechiness acousticness instrumentalness liveness valence tempo time_signature Duration(ms) Album_Number Artist_Popularity Artist_Followers Popular
0 38 0.800 0.722 1 -7.854 0 0.0861 0.0004 0.902 0.1010 0.505 127.003 4 416692 1 58 200007 1
1 33 0.774 0.686 8 -6.763 1 0.0670 0.0001 0.882 0.0902 0.068 126.026 4 541428 2 58 200007 1
2 28 0.800 0.863 1 -7.198 1 0.0569 0.0016 0.900 0.0773 0.155 124.007 4 408115 3 58 200007 1
3 30 0.803 0.645 7 -8.949 1 0.0808 0.0015 0.847 0.0996 0.409 123.991 4 482419 4 58 200007 1
4 20 0.537 0.941 5 -7.788 0 0.0320 0.0000 0.873 0.0709 0.420 132.000 4 484747 1 58 200007 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
50780 1 0.623 0.876 5 -6.846 1 0.0364 0.0001 0.889 0.1460 0.424 123.994 4 484400 2 11 638 0
50781 17 0.687 0.737 4 -6.577 0 0.0910 0.0154 0.876 0.1110 0.525 134.998 4 441128 1 14 1322 1
50782 13 0.602 0.825 6 -4.665 1 0.1520 0.0094 0.898 0.0859 0.457 128.002 4 369843 2 14 1322 1
50783 12 0.727 0.788 1 -6.680 1 0.0713 0.0063 0.797 0.1090 0.237 126.019 4 353280 3 14 1322 1
50784 12 0.802 0.632 3 -8.077 0 0.1020 0.0028 0.854 0.1140 0.143 130.025 4 366240 4 14 1322 1

50785 rows × 18 columns

EDA

Word Cloud of Artist Genres

In [38]:
from wordcloud import WordCloud
plt.figure(figsize=(14, 13))

text = " ".join(x for i in df.Artist_Genres for x in i)
wordcloud = WordCloud(background_color="black").generate(text)
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()
In [32]:
df.sort_values(by=['Track_Popularity'],ascending = False).loc[:,["Track_Name","Artist_Name","Track_Popularity"]].head(n=10)
Out[32]:
Track_Name Artist_Name Track_Popularity
22229 Sky and Sand Paul Kalkbrenner 65
25894 Sandmann le Shuuk 65
22313 No Goodbye Paul Kalkbrenner 64
20657 Blurred Kiasmos 64
29184 Midnight (The Hanging Tree) HOSH 63
20997 A New Error Moderat 62
22159 Return to Oz - ARTBAT Remix ARTBAT 62
1008 Elements Joyhauser 60
20664 Looped Kiasmos 60
21517 Dreamer's Wake Rival Consoles 59
In [36]:
df.loc[:,["Artist_Name","Artist_Popularity"]].groupby(["Artist_Name"]).max().sort_values(by=['Artist_Popularity'],ascending = False).head(n=10)
Out[36]:
Artist_Popularity
Artist_Name
Paul Kalkbrenner 69
Solomun 65
Moderat 63
Christian Lffler 63
Fritz Kalkbrenner 63
Kiasmos 62
Klsch 62
Oliver Koletzki 62
Worakls 62
Green Velvet 62

Track Count Histogram by Artists

In [8]:
tr = df.loc[:,["Artist_Name","Artist_Popularity"]].groupby(["Artist_Name"]).count().rename(columns={'Artist_Popularity': 'Count'})
tr = tr.sort_values(by=['Count'],ascending = False).head(n=15).reset_index()

import plotly.express as px
fig = px.bar(tr, x='Artist_Name', y='Count')
fig.show()

Tempo

In [13]:
sns.set(style="whitegrid")
plt.figure(figsize=(16, 8))
sns.kdeplot(ftracks.tempo,shade = True,linewidth = 5)
Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x244895b8438>

Danceability

In [14]:
plt.figure(figsize=(16, 8))
sns.kdeplot(ftracks.danceability,shade = True, linewidth = 5)
Out[14]:
<matplotlib.axes._subplots.AxesSubplot at 0x24489675240>

Acousticness

In [15]:
plt.figure(figsize=(16, 8))
sns.kdeplot(ftracks.acousticness,shade = True, linewidth = 5)
Out[15]:
<matplotlib.axes._subplots.AxesSubplot at 0x24489783c18>

Instrumentalness

In [16]:
plt.figure(figsize=(16, 8))
sns.kdeplot(ftracks.instrumentalness,shade = True, linewidth = 5)
Out[16]:
<matplotlib.axes._subplots.AxesSubplot at 0x244897eabe0>

Loudness

In [17]:
plt.figure(figsize=(16, 8))
sns.kdeplot(ftracks.loudness,shade = True, linewidth = 5)
Out[17]:
<matplotlib.axes._subplots.AxesSubplot at 0x2448a0c8c18>

Valence

In [19]:
plt.figure(figsize=(16, 8))
display(sns.kdeplot(ftracks.valence,shade = True, linewidth = 5))
<matplotlib.axes._subplots.AxesSubplot at 0x2448a10bb70>

Artist Popularity

In [20]:
plt.figure(figsize=(16, 8))
display(sns.kdeplot(ftracks.Artist_Popularity,shade = True, linewidth = 5))
<matplotlib.axes._subplots.AxesSubplot at 0x244957f6400>

Pairplot

In [63]:
slicer = list(range(1, 12)) + [15,16,17]
plt.figure(figsize=(16, 8))
sns.set(style="ticks",color_codes=True)
sns.pairplot(ftracks.iloc[:,slicer], hue="Popular")
Out[63]:
<seaborn.axisgrid.PairGrid at 0x15fb35e64a8>
<Figure size 1152x576 with 0 Axes>

Correlation and Collinearity

In [5]:
cor = ftracks.corr()

plt.figure(figsize=(14, 14))
ax = sns.heatmap(
    cor, 
    vmin=-1, vmax=1, center=0,
    cmap=sns.diverging_palette(20, 220, n=200),
    square=True, annot = True
)
ax.set_xticklabels(
    ax.get_xticklabels(),
    rotation=45,
    horizontalalignment='right'
)
ax.set_ylim(len(cor)+0.5, -0.5);
In [7]:
cor["Popular"]
Out[7]:
Track_Popularity     0.726581
danceability        -0.034738
energy              -0.070082
key                 -0.016198
loudness            -0.024776
mode                -0.018251
speechiness         -0.054541
acousticness         0.051570
instrumentalness    -0.032563
liveness            -0.001136
valence              0.001151
tempo               -0.045479
time_signature       0.008842
Duration(ms)        -0.006838
Album_Number        -0.046733
Artist_Popularity    0.427343
Artist_Followers     0.297156
Popular              1.000000
Name: Popular, dtype: float64

Corelation between features Popular cloumn is diplayed in the heatmap and table above. There isn't any high colineratiy seen between features (>0.8).

ML Aplication

Mainly, 5 supervised classification method is planned to be use for predicting the track popularity. These methods are Logistic Regression, Decision Trees, Support Vector Machine, Random Forests and XGBoost Classifier. All supervised machine learning algorithms this project went through the same steps. This steps can be summarised as follows:

  • Model building with pre-selected features in chi-square analysis
  • Cross validation score analysis of selected features with chi-square (X_train_s, X_test_s)
  • Cross validation score analysis of all features(X_train, X_test) and selecting features according to their importance for given algorithm
  • Comparing the results of cv scores and selecting model
  • Hyperparameter tuning for selected model
  • Generating Confusion Matrix, ROC Curve and Precision-Recall Curve
  • Extracting the model result values:
    • Train / Test Accuracy
    • Precision, Recall, F1Score
    • AUC, Refit Time

Preprocessing

In [9]:
#Random state definition
rnd = 50

#Shuffling data
shf = ftracks.sample(frac=1,random_state = rnd).reset_index(drop=True)

#Creating features and labels
X = shf.drop(['Track_Popularity',"Popular"],axis=1)
y = shf.loc[:,"Popular"]


#Anolmaly detection using IsolationForest
clf = IsolationForest(max_samples=100, random_state=rnd, contamination=.1)
clf.fit(X)
y_pred_train = clf.predict(X)
X_rem = X[np.where(y_pred_train == 1, True, False)]
y_rem = y[X_rem.index]

#Splitting data
X_train, X_test, y_train, y_test = train_test_split(X_rem, y_rem, test_size=0.25, random_state=rnd)

#Scaling Data
scaler = MinMaxScaler().fit(X_train)
X_train_s = scaler.transform(X_train)
X_test_s = scaler.transform(X_test)

#Final Data
X_train = pd.DataFrame(X_train_s, columns=X.columns)
X_test = pd.DataFrame(X_test_s, columns=X.columns)
In [98]:
print(X_train.shape)
(34279, 16)

Feature Selection with Chi-Square

For feature selection, basically, I tried 2 methods. Firstly, I analysed the Chi-Square values of the features and selected features based on Chi-Square p values and correlation matrix. Secondly, I tried to find feature importance for each method and selected the features according to their importance in the given method.

In [10]:
selector_chi = SelectKBest(chi2, k=X_train.shape[1]).fit(X_train, y_train)


selection_chi = {"Columns": X_train.columns.tolist(),
                 "Scores" : selector_chi.scores_,
                 "P_values" : selector_chi.pvalues_  }

selection_chi = pd.DataFrame(selection_chi).sort_values(by='Scores', ascending=False).reset_index()
display(selection_chi)


#Selecting columns which p-value
threshold = 0.4
selected_features = selection_chi[selection_chi.P_values<threshold].Columns.tolist()
print("For defined threshold, {} features are selected. \n These features are: {}".format(len(selected_features),selected_features))
index Columns Scores P_values
0 14 Artist_Popularity 526.390785 1.722495e-116
1 15 Artist_Followers 466.934129 1.489118e-103
2 13 Album_Number 26.565210 2.547908e-07
3 5 speechiness 6.578479 1.032191e-02
4 6 acousticness 5.087278 2.410199e-02
5 1 energy 4.496407 3.396615e-02
6 7 instrumentalness 3.517409 6.072732e-02
7 2 key 2.288817 1.303092e-01
8 4 mode 1.488141 2.225051e-01
9 9 valence 1.437684 2.305144e-01
10 10 tempo 0.716015 3.974541e-01
11 11 time_signature 0.131368 7.170181e-01
12 0 danceability 0.014962 9.026462e-01
13 12 Duration(ms) 0.012294 9.117127e-01
14 3 loudness 0.008876 9.249391e-01
15 8 liveness 0.008212 9.277941e-01
For defined threshold, 11 features are selected. 
 These features are: ['Artist_Popularity', 'Artist_Followers', 'Album_Number', 'speechiness', 'acousticness', 'energy', 'instrumentalness', 'key', 'mode', 'valence', 'tempo']

In the corelation section, it seems that loudness have higher corelation than some of the features which selected in the above. Thus, I decided to add these feature to selected columns. In the analysis, we waw that adding loudness feature increases the accuracy.

In [11]:
# New features
X_train_s = X_train.loc[:,selected_features + ['loudness']]
X_test_s = X_test.loc[:,selected_features + ['loudness']]

Logistic Regression

Selected Features with Chi-Square and Corelation

In [12]:
models = LogisticRegression(random_state=rnd,n_jobs=-1)
cv = cross_validate(models,X_train_s,y_train,cv = 3, n_jobs=-1, return_estimator=True)
print("Mean training accuracy: {}".format(np.mean(cv['test_score'])))
print("Test accuracy: {}".format(cv["estimator"][0].score(X_test_s,y_test)))
Mean training accuracy: 0.6988534994394334
Test accuracy: 0.6987835827426271

Feature Selection with RFE

In [13]:
high_score=0
nof=0           
score_list =[]
cv_list=[]
for n in range(1,len(X_train.columns)+1):
    model = LogisticRegression(random_state=rnd,n_jobs=-1)
    rfe = RFE(model,n)
    X_train_rfe = rfe.fit_transform(X_train,y_train)
    X_test_rfe = rfe.transform(X_test)
    cv = cross_validate(model,X_train_rfe,y_train,cv = 3, n_jobs=-1, return_estimator=True)
    score = cv["estimator"][0].score(X_test_rfe,y_test)
    cv_s = np.mean(cv['test_score'])
    score_list.append(score)
    cv_list.append(cv_s)
    if(cv_s>high_score):
        high_score = cv_s
        nof = n
        test_s = score
        sup = rfe.get_support(indices=True)

print("Optimum number of features: {}".format(nof))
print('Selected features are:',X_train.iloc[:,sup].columns.tolist(),"\n")
print('Mean training accuracy with {} features: {:.6f}'.format(nof, high_score))
print("Test accuracy with {} features: {:.6f}".format(nof, test_s))
Optimum number of features: 15
Selected features are: ['danceability', 'energy', 'key', 'loudness', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', 'time_signature', 'Duration(ms)', 'Album_Number', 'Artist_Popularity', 'Artist_Followers'] 

Mean training accuracy with 15 features: 0.699583
Test accuracy with 15 features: 0.699659

Both training and test accuracy of RFE features are higher than chi-square features. Thus, I selected the RFE features as independent variables for hyperparameter tuning process.

Hyperparameter Tuning

In [16]:
#New dataset after RFE
X_train_log = X_train.iloc[:,sup]
X_test_log = X_test.iloc[:,sup]

#Hyperparameter tuning
param_log = { "penalty": ["l1", "l2", "elasticnet"],
             "C": [0.01,0.1,1,5,10],
             "tol": [1e-3,1e-2,1e-1],
             "solver": ["newton-cg","lbfgs","liblinear","sag"],
             "max_iter": [50,100] }


log_reg = LogisticRegression(random_state=rnd,n_jobs=-1)


clf_log = GridSearchCV(log_reg,param_log, cv=5, n_jobs = -1)
clf_log.fit(X_train_log, y_train)

print("Tuned Logistic Regression Classification Parameters: {}".format(clf_log.best_params_)) 
print("Mean of the cv scores is {:.6f}".format(clf_log.best_score_))
print("Test Score {:.6f}".format(clf_log.score(X_test_log,y_test)))
print("Seconds used for refitting the best model on the train dataset: {:.6f}".format(clf_log.refit_time_))
Tuned Logistic Regression Classification Parameters: {'C': 5, 'max_iter': 50, 'penalty': 'l2', 'solver': 'sag', 'tol': 0.01}
Mean of the cv scores is 0.699991
Test Score 0.701146
Seconds used for refitting the best model on the train dataset: 0.212121

Confusion Matrix

In [17]:
#Confusion Matrix
y_pred_log = clf_log.predict(X_test_log)
conf = confusion_matrix(y_test,y_pred_log)

plt.figure(figsize=(16, 8))
ax = sns.heatmap(conf, annot=True,cmap='Blues',fmt='g',annot_kws={"size": 13})
# labels, title and ticks
ax.set_xlabel('Predicted Labels',fontsize = 15)
ax.set_ylabel('True Labels',fontsize = 15)
ax.set_title('Confusion Matrix of Logistic Regression',fontsize = 20)
ax.xaxis.set_ticklabels(['Not Popular', 'Popular'],fontsize = 12)
ax.yaxis.set_ticklabels(['Not Popular', 'Popular'],fontsize = 12)
plt.show()

ROC Curve

In [18]:
y_pred_prop = clf_log.predict_proba(X_test_log)[:,1]
fpr_log, tpr_log, _ = roc_curve(y_test, y_pred_prop)
roc_auc_log = auc(fpr_log, tpr_log)
sns.set_style("white")
plt.figure(figsize=(14, 10))
plt.plot(fpr_log, tpr_log, color='darkorange',
         label='ROC curve (area = %0.2f)' % roc_auc_log)
plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate',fontsize=18,labelpad =10)
plt.ylabel('True Positive Rate',fontsize=18)
plt.title('Receiver Operating Characteristic',fontsize=22).set_position([.5, 1.02])
plt.legend(loc="lower right",fontsize=13)
plt.show()

Precision-Recall Tradeoff

In [19]:
lr_precision, lr_recall, _ = precision_recall_curve(y_test, y_pred_prop)
no_skill = len(y_test[y_test==1]) / len(y_test)

plt.figure(figsize=(14, 10))
plt.plot(lr_recall, lr_precision, color='red',
         label='Logistic' % roc_auc_log)
plt.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')
plt.xlabel('Recall',fontsize=18,labelpad =10)
plt.ylabel('Precision',fontsize=18)
plt.title('Logistic Regression Precision-Recall Curve',fontsize=22).set_position([.5, 1.02])
plt.show()

Results

In [20]:
train_ac_log = clf_log.best_score_
accuracy_log = accuracy_score(y_test,y_pred_log)
precision_log = precision_score(y_test,y_pred_log)
recall_log = recall_score(y_test,y_pred_log)
f1_score_log = f1_score(y_test,y_pred_log)
time_log = clf_log.refit_time_

logistic_result = [train_ac_log,accuracy_log,precision_log,recall_log,f1_score_log,roc_auc_log,time_log]

#Accuracy table generation
res_col = ['Train Accuracy','Test Accuracy','Precision','Recall','F1 Score','AUC','Fitting Time']

log_result = np.array(logistic_result).reshape(1,-1)

f_log = pd.DataFrame(log_result,index=['Logistic Regression'],columns = res_col)
f_log
Out[20]:
Train Accuracy Test Accuracy Precision Recall F1 Score AUC Fitting Time
Logistic Regression 0.699991 0.701146 0.687939 0.624083 0.654457 0.765147 0.212121

Decision Tree

Selected Features with Chi-Square and Corelation

In [21]:
models = DecisionTreeClassifier(random_state = rnd)
cv = cross_validate(models,X_train_s,y_train,cv = 3, n_jobs=-1, return_estimator=True)
print("Mean training accuracy: {}".format(np.mean(cv['test_score'])))
print("Test accuracy: {}".format(cv["estimator"][0].score(X_test_s,y_test)))
Mean training accuracy: 0.6351118694976694
Test accuracy: 0.6313993174061433

Feature Selection for Decision Tree

With all data:

In [74]:
models = DecisionTreeClassifier(random_state = rnd)
cv = cross_validate(models,X_train,y_train,cv = 3, n_jobs=-1, return_estimator=True)
print("Mean training accuracy: {}".format(np.mean(cv['test_score'])))
print("Test accuracy: {}".format(cv["estimator"][0].score(X_test,y_test)))
Mean training accuracy: 0.6337992987834765
Test accuracy: 0.6374376476765555
In [99]:
fi_dt = pd.DataFrame({'Feature':X_train.columns,
       'Importance':cv["estimator"][0].feature_importances_}).sort_values(by = "Importance",ascending = True).reset_index(drop = True)
       
fig = px.bar(fi_dt, x='Importance', y='Feature',orientation='h',color='Importance')
fig.show()
In [27]:
val_t = 0
val_s = 0

fi_dt = fi_dt.sort_values(by = "Importance",ascending = False)
for i in fi_dt.index:
    cols = fi_dt.Feature[0:i+1]
    models = DecisionTreeClassifier(random_state = rnd)
    cv = cross_validate(models, X_train.loc[:,cols] ,y_train,cv = 3, n_jobs=-1, return_estimator=True)
    tr = np.mean(cv['test_score'])
    ts = cv["estimator"][0].score(X_test.loc[:,cols],y_test)
    if tr > val_t:
        val_t = tr
        names = cols.tolist()
        val_s = ts
        nof = i + 1
        
print("Optimum number of features: {}".format(nof))
print('Selected features are:',names,"\n")
print('Mean training accuracy with {} features: {:.6f}'.format(nof, val_t))
print("Test accuracy with {} features: {:.6f}".format(nof, val_s))
Optimum number of features: 2
Selected features are: ['Artist_Popularity', 'Artist_Followers'] 

Mean training accuracy with 2 features: 0.732664
Test accuracy with 2 features: 0.730900

Both training and test accuracy of selected features by their importance are higher than chi-square features. Thus, I selected the 2 columns as independent variables for hyperparameter tuning process.

Hyper Paramater Tuning

In [76]:
X_train_dt = X_train.loc[:,names]
X_test_dt = X_test.loc[:,names]

param_dt = {"criterion":["gini", "entropy"],
            "splitter" : ["best", "random"],
            "min_samples_split": [2,10,30,50,100,200,500],
            "ccp_alpha": [0,1e-3,0.01,0.1,1,5],    
}


dt = DecisionTreeClassifier(random_state = rnd)


clf_dt = GridSearchCV(dt,param_dt, cv=5, n_jobs = -1)
clf_dt.fit(X_train_dt, y_train)

print("Tuned Decision Tree Classification Parameters: {}".format(clf_dt.best_params_)) 
print("Mean of the cv scores is {:.6f}".format(clf_dt.best_score_))
print("Test Score {:.6f}".format(clf_dt.score(X_test_dt,y_test)))
print("Seconds used for refitting the best model on the whole dataset: {:.6f}".format(clf_dt.refit_time_))
Tuned Decision Tree Classification Parameters: {'ccp_alpha': 0, 'criterion': 'entropy', 'min_samples_split': 2, 'splitter': 'best'}
Mean of the cv scores is 0.735932
Test Score 0.739827
Seconds used for refitting the best model on the whole dataset: 0.056928

Visualization

In [32]:
import os
from sklearn.tree import export_graphviz
os.environ["PATH"] += ';' + r'C:\Users\Dell\Anaconda3\Library\bin\graphviz'
import graphviz


dot_data = export_graphviz(clf_dt.best_estimator_, out_file=None,
                     feature_names=names,
                     class_names=["Not Popular","Popular"],
                     filled=True, rounded=True,
                     special_characters=True)
graph = graphviz.Source(dot_data)
graph
Out[32]:
Tree 0 Artist_Popularity ≤ 0.532 entropy = 0.995 samples = 34279 value = [18583, 15696] class = Not Popular 1 Artist_Popularity ≤ 0.325 entropy = 0.875 samples = 19261 value = [13585, 5676] class = Not Popular 0->1 True 1602 Artist_Followers ≤ 0.099 entropy = 0.918 samples = 15018 value = [4998, 10020] class = Popular 0->1602 False 2 Artist_Popularity ≤ 0.167 entropy = 0.677 samples = 8121 value = [6671, 1450] class = Not Popular 1->2 1041 Artist_Followers ≤ 0.074 entropy = 0.958 samples = 11140 value = [6914, 4226] class = Not Popular 1->1041 3 Artist_Followers ≤ 0.0 entropy = 0.552 samples = 2418 value = [2108, 310] class = Not Popular 2->3 500 Artist_Followers ≤ 0.003 entropy = 0.722 samples = 5703 value = [4563, 1140] class = Not Popular 2->500 4 Artist_Popularity ≤ 0.071 entropy = 0.731 samples = 606 value = [482, 124] class = Not Popular 3->4 213 Artist_Popularity ≤ 0.04 entropy = 0.477 samples = 1812 value = [1626, 186] class = Not Popular 3->213 5 Artist_Followers ≤ 0.0 entropy = 0.633 samples = 320 value = [269, 51] class = Not Popular 4->5 130 Artist_Followers ≤ 0.0 entropy = 0.819 samples = 286 value = [213, 73] class = Not Popular 4->130 6 Artist_Followers ≤ 0.0 entropy = 0.762 samples = 181 value = [141, 40] class = Not Popular 5->6 93 Artist_Followers ≤ 0.0 entropy = 0.399 samples = 139 value = [128, 11] class = Not Popular 5->93 7 entropy = 0.0 samples = 5 value = [5, 0] class = Not Popular 6->7 8 Artist_Popularity ≤ 0.056 entropy = 0.773 samples = 176 value = [136, 40] class = Not Popular 6->8 9 Artist_Followers ≤ 0.0 entropy = 0.791 samples = 164 value = [125, 39] class = Not Popular 8->9 90 Artist_Followers ≤ 0.0 entropy = 0.414 samples = 12 value = [11, 1] class = Not Popular 8->90 10 Artist_Followers ≤ 0.0 entropy = 0.745 samples = 137 value = [108, 29] class = Not Popular 9->10 77 Artist_Followers ≤ 0.0 entropy = 0.951 samples = 27 value = [17, 10] class = Not Popular 9->77 11 Artist_Popularity ≤ 0.04 entropy = 0.763 samples = 131 value = [102, 29] class = Not Popular 10->11 76 entropy = 0.0 samples = 6 value = [6, 0] class = Not Popular 10->76 12 Artist_Popularity ≤ 0.008 entropy = 0.784 samples = 120 value = [92, 28] class = Not Popular 11->12 71 Artist_Followers ≤ 0.0 entropy = 0.439 samples = 11 value = [10, 1] class = Not Popular 11->71 13 Artist_Followers ≤ 0.0 entropy = 0.658 samples = 47 value = [39, 8] class = Not Popular 12->13 34 Artist_Followers ≤ 0.0 entropy = 0.847 samples = 73 value = [53, 20] class = Not Popular 12->34 14 Artist_Followers ≤ 0.0 entropy = 0.523 samples = 34 value = [30, 4] class = Not Popular 13->14 29 Artist_Followers ≤ 0.0 entropy = 0.89 samples = 13 value = [9, 4] class = Not Popular 13->29 15 Artist_Followers ≤ 0.0 entropy = 0.787 samples = 17 value = [13, 4] class = Not Popular 14->15 28 entropy = 0.0 samples = 17 value = [17, 0] class = Not Popular 14->28 16 Artist_Followers ≤ 0.0 entropy = 0.592 samples = 14 value = [12, 2] class = Not Popular 15->16 25 Artist_Followers ≤ 0.0 entropy = 0.918 samples = 3 value = [1, 2] class = Popular 15->25 17 entropy = 0.0 samples = 2 value = [2, 0] class = Not Popular 16->17 18 Artist_Followers ≤ 0.0 entropy = 0.65 samples = 12 value = [10, 2] class = Not Popular 16->18 19 Artist_Followers ≤ 0.0 entropy = 0.722 samples = 10 value = [8, 2] class = Not Popular 18->19 24 entropy = 0.0 samples = 2 value = [2, 0] class = Not Popular 18->24 20 entropy = 0.811 samples = 4 value = [3, 1] class = Not Popular 19->20 21 Artist_Followers ≤ 0.0 entropy = 0.65 samples = 6 value = [5, 1] class = Not Popular 19->21 22 entropy = 0.0 samples = 2 value = [2, 0] class = Not Popular 21->22 23 entropy = 0.811 samples = 4 value = [3, 1] class = Not Popular 21->23 26 entropy = 0.0 samples = 1 value = [0, 1] class = Popular 25->26 27 entropy = 1.0 samples = 2 value = [1, 1] class = Not Popular 25->27 30 entropy = 1.0 samples = 4 value = [2, 2] class = Not Popular 29->30 31 Artist_Followers ≤ 0.0 entropy = 0.764 samples = 9 value = [7, 2] class = Not Popular 29->31 32 entropy = 0.0 samples = 3 value = [3, 0] class = Not Popular 31->32 33 entropy = 0.918 samples = 6 value = [4, 2] class = Not Popular 31->33 35 Artist_Followers ≤ 0.0 entropy = 0.881 samples = 60 value = [42, 18] class = Not Popular 34->35 68 Artist_Followers ≤ 0.0 entropy = 0.619 samples = 13 value = [11, 2] class = Not Popular 34->68 36 Artist_Followers ≤ 0.0 entropy = 0.866 samples = 59 value = [42, 17] class = Not Popular 35->36 67 entropy = 0.0 samples = 1 value = [0, 1] class = Popular 35->67 37 Artist_Followers ≤ 0.0 entropy = 0.879 samples = 57 value = [40, 17] class = Not Popular 36->37 66 entropy = 0.0 samples = 2 value = [2, 0] class = Not Popular 36->66 38 Artist_Followers ≤ 0.0 entropy = 0.848 samples = 51 value = [37, 14] class = Not Popular 37->38 61 Artist_Popularity ≤ 0.024 entropy = 1.0 samples = 6 value = [3, 3] class = Not Popular 37->61 39 Artist_Followers ≤ 0.0 entropy = 0.961 samples = 13 value = [8, 5] class = Not Popular 38->39 48 Artist_Followers ≤ 0.0 entropy = 0.79 samples = 38 value = [29, 9] class = Not Popular 38->48 40 Artist_Followers ≤ 0.0 entropy = 0.845 samples = 11 value = [8, 3] class = Not Popular 39->40 47 entropy = 0.0 samples = 2 value = [0, 2] class = Popular 39->47 41 Artist_Followers ≤ 0.0 entropy = 0.764 samples = 9 value = [7, 2] class = Not Popular 40->41 46 entropy = 1.0 samples = 2 value = [1, 1] class = Not Popular 40->46 42 Artist_Followers ≤ 0.0 entropy = 0.811 samples = 8 value = [6, 2] class = Not Popular 41->42 45 entropy = 0.0 samples = 1 value = [1, 0] class = Not Popular 41->45 43 entropy = 0.811 samples = 4 value = [3, 1] class = Not Popular 42->43 44 entropy = 0.811 samples = 4 value = [3, 1] class = Not Popular 42->44 49 Artist_Followers ≤ 0.0 entropy = 0.61 samples = 20 value = [17, 3] class = Not Popular 48->49 52 Artist_Followers ≤ 0.0 entropy = 0.918 samples = 18 value = [12, 6] class = Not Popular 48->52 50 entropy = 0.684 samples = 11 value = [9, 2] class = Not Popular 49->50 51 entropy = 0.503 samples = 9 value = [8, 1] class = Not Popular 49->51 53 Artist_Followers ≤ 0.0 entropy = 0.98 samples = 12 value = [7, 5] class = Not Popular 52->53 60 entropy = 0.65 samples = 6 value = [5, 1] class = Not Popular 52->60 54 entropy = 1.0 samples = 4 value = [2, 2] class = Not Popular 53->54 55 Artist_Followers ≤ 0.0 entropy = 0.954 samples = 8 value = [5, 3] class = Not Popular 53->55 56 entropy = 0.0 samples = 1 value = [1, 0] class = Not Popular 55->56 57 Artist_Followers ≤ 0.0 entropy = 0.985 samples = 7 value = [4, 3] class = Not Popular 55->57 58 entropy = 1.0 samples = 4 value = [2, 2] class = Not Popular 57->58 59 entropy = 0.918 samples = 3 value = [2, 1] class = Not Popular 57->59 62 entropy = 0.0 samples = 1 value = [0, 1] class = Popular 61->62 63 Artist_Followers ≤ 0.0 entropy = 0.971 samples = 5 value = [3, 2] class = Not Popular 61->63 64 entropy = 1.0 samples = 2 value = [1, 1] class = Not Popular 63->64 65 entropy = 0.918 samples = 3 value = [2, 1] class = Not Popular 63->65 69 entropy = 0.0 samples = 8 value = [8, 0] class = Not Popular 68->69 70 entropy = 0.971 samples = 5 value = [3, 2] class = Not Popular 68->70 72 entropy = 0.0 samples = 6 value = [6, 0] class = Not Popular 71->72 73 Artist_Followers ≤ 0.0 entropy = 0.722 samples = 5 value = [4, 1] class = Not Popular 71->73 74 entropy = 1.0 samples = 2 value = [1, 1] class = Not Popular 73->74 75 entropy = 0.0 samples = 3 value = [3, 0] class = Not Popular 73->75 78 Artist_Popularity ≤ 0.016 entropy = 0.863 samples = 7 value = [2, 5] class = Popular 77->78 85 Artist_Followers ≤ 0.0 entropy = 0.811 samples = 20 value = [15, 5] class = Not Popular 77->85 79 entropy = 0.0 samples = 1 value = [0, 1] class = Popular 78->79 80 Artist_Popularity ≤ 0.04 entropy = 0.918 samples = 6 value = [2, 4] class = Popular 78->80 81 entropy = 1.0 samples = 2 value = [1, 1] class = Not Popular 80->81 82 Artist_Followers ≤ 0.0 entropy = 0.811 samples = 4 value = [1, 3] class = Popular 80->82 83 entropy = 0.0 samples = 1 value = [0, 1] class = Popular 82->83 84 entropy = 0.918 samples = 3 value = [1, 2] class = Popular 82->84 86 Artist_Popularity ≤ 0.024 entropy = 0.881 samples = 10 value = [7, 3] class = Not Popular 85->86 89 entropy = 0.722 samples = 10 value = [8, 2] class = Not Popular 85->89 87 entropy = 1.0 samples = 2 value = [1, 1] class = Not Popular 86->87 88 entropy = 0.811 samples = 8 value = [6, 2] class = Not Popular 86->88 91 entropy = 0.0 samples = 1 value = [0, 1] class = Popular 90->91 92 entropy = 0.0 samples = 11 value = [11, 0] class = Not Popular 90->92 94 Artist_Followers ≤ 0.0 entropy = 0.169 samples = 40 value = [39, 1] class = Not Popular 93->94 99 Artist_Followers ≤ 0.0 entropy = 0.472 samples = 99 value = [89, 10] class = Not Popular 93->99 95 Artist_Followers ≤ 0.0 entropy = 0.414 samples = 12 value = [11, 1] class = Not Popular 94->95 98 entropy = 0.0 samples = 28 value = [28, 0] class = Not Popular 94->98 96 entropy = 0.0 samples = 6 value = [6, 0] class = Not Popular 95->96 97 entropy = 0.65 samples = 6 value = [5, 1] class = Not Popular 95->97 100 Artist_Followers ≤ 0.0 entropy = 0.414 samples = 84 value = [77, 7] class = Not Popular 99->100 123 Artist_Followers ≤ 0.0 entropy = 0.722 samples = 15 value = [12, 3] class = Not Popular 99->123 101 Artist_Followers ≤ 0.0 entropy = 0.488 samples = 66 value = [59, 7] class = Not Popular 100->101 122 entropy = 0.0 samples = 18 value = [18, 0] class = Not Popular 100->122 102 Artist_Popularity ≤ 0.04 entropy = 0.378 samples = 41 value = [38, 3] class = Not Popular 101->102 111 Artist_Followers ≤ 0.0 entropy = 0.634 samples = 25 value = [21, 4] class = Not Popular 101->111 103 entropy = 0.0 samples = 15 value = [15, 0] class = Not Popular 102->103 104 Artist_Followers ≤ 0.0 entropy = 0.516 samples = 26 value = [23, 3] class = Not Popular 102->104 105 Artist_Popularity ≤ 0.056 entropy = 0.592 samples = 21 value = [18, 3] class = Not Popular 104->105 110 entropy = 0.0 samples = 5 value = [5, 0] class = Not Popular 104->110 106 entropy = 0.811 samples = 4 value = [3, 1] class = Not Popular 105->106 107 Artist_Followers ≤ 0.0 entropy = 0.523 samples = 17 value = [15, 2] class = Not Popular 105->107 108 entropy = 0.544 samples = 8 value = [7, 1] class = Not Popular 107->108 109 entropy = 0.503 samples = 9 value = [8, 1] class = Not Popular 107->109 112 entropy = 0.0 samples = 1 value = [0, 1] class = Popular 111->112 113 Artist_Followers ≤ 0.0 entropy = 0.544 samples = 24 value = [21, 3] class = Not Popular 111->113 114 entropy = 0.0 samples = 2 value = [2, 0] class = Not Popular 113->114 115 Artist_Popularity ≤ 0.016 entropy = 0.575 samples = 22 value = [19, 3] class = Not Popular 113->115 116 Artist_Followers ≤ 0.0 entropy = 0.503 samples = 9 value = [8, 1] class = Not Popular 115->116 119 Artist_Followers ≤ 0.0 entropy = 0.619 samples = 13 value = [11, 2] class = Not Popular 115->119 117 entropy = 0.592 samples = 7 value = [6, 1] class = Not Popular 116->117 118 entropy = 0.0 samples = 2 value = [2, 0] class = Not Popular 116->118 120 entropy = 0.65 samples = 6 value = [5, 1] class = Not Popular 119->120 121 entropy = 0.592 samples = 7 value = [6, 1] class = Not Popular 119->121 124 entropy = 0.918 samples = 3 value = [2, 1] class = Not Popular 123->124 125 Artist_Followers ≤ 0.0 entropy = 0.65 samples = 12 value = [10, 2] class = Not Popular 123->125 126 entropy = 0.0 samples = 1 value = [1, 0] class = Not Popular 125->126 127 Artist_Popularity ≤ 0.056 entropy = 0.684 samples = 11 value = [9, 2] class = Not Popular 125->127 128 entropy = 0.65 samples = 6 value = [5, 1] class = Not Popular 127->128 129 entropy = 0.722 samples = 5 value = [4, 1] class = Not Popular 127->129 131 Artist_Popularity ≤ 0.103 entropy = 0.88 samples = 144 value = [101, 43] class = Not Popular 130->131 184 Artist_Followers ≤ 0.0 entropy = 0.744 samples = 142 value = [112, 30] class = Not Popular 130->184 132 Artist_Popularity ≤ 0.087 entropy = 0.968 samples = 71 value = [43, 28] class = Not Popular 131->132 157 Artist_Followers ≤ 0.0 entropy = 0.733 samples = 73 value = [58, 15] class = Not Popular 131->157 133 Artist_Followers ≤ 0.0 entropy = 0.936 samples = 54 value = [35, 19] class = Not Popular 132->133 150 Artist_Followers ≤ 0.0 entropy = 0.998 samples = 17 value = [8, 9] class = Popular 132->150 134 Artist_Followers ≤ 0.0 entropy = 0.738 samples = 24 value = [19, 5] class = Not Popular 133->134 145 Artist_Followers ≤ 0.0 entropy = 0.997 samples = 30 value = [16, 14] class = Not Popular 133->145 135 Artist_Followers ≤ 0.0 entropy = 0.831 samples = 19 value = [14, 5] class = Not Popular 134->135 144 entropy = 0.0 samples = 5 value = [5, 0] class = Not Popular 134->144 136 Artist_Followers ≤ 0.0 entropy = 0.89 samples = 13 value = [9, 4] class = Not Popular 135->136 141 Artist_Followers ≤ 0.0 entropy = 0.65 samples = 6 value = [5, 1] class = Not Popular 135->141 137 entropy = 0.722 samples = 5 value = [4, 1] class = Not Popular 136->137 138 Artist_Followers ≤ 0.0 entropy = 0.954 samples = 8 value = [5, 3] class = Not Popular 136->138 139 entropy = 0.918 samples = 3 value = [2, 1] class = Not Popular 138->139 140 entropy = 0.971 samples = 5 value = [3, 2] class = Not Popular 138->140 142 entropy = 0.0 samples = 2 value = [2, 0] class = Not Popular 141->142 143 entropy = 0.811 samples = 4 value = [3, 1] class = Not Popular 141->143 146 entropy = 0.98 samples = 12 value = [7, 5] class = Not Popular 145->146 147 Artist_Followers ≤ 0.0 entropy = 1.0 samples = 18 value = [9, 9] class = Not Popular 145->147 148 entropy = 1.0 samples = 6 value = [3, 3] class = Not Popular 147->148 149 entropy = 1.0 samples = 12 value = [6, 6] class = Not Popular 147->149 151 Artist_Followers ≤ 0.0 entropy = 0.918 samples = 9 value = [3, 6] class = Popular 150->151 156 entropy = 0.954 samples = 8 value = [5, 3] class = Not Popular 150->156 152 Artist_Followers ≤ 0.0 entropy = 0.971 samples = 5 value = [2, 3] class = Popular 151->152 155 entropy = 0.811 samples = 4 value = [1, 3] class = Popular 151->155 153 entropy = 0.918 samples = 3 value = [1, 2] class = Popular 152->153 154 entropy = 1.0 samples = 2 value = [1, 1] class = Not Popular 152->154 158 Artist_Followers ≤ 0.0 entropy = 0.792 samples = 63 value = [48, 15] class = Not Popular 157->158 183 entropy = 0.0 samples = 10 value = [10, 0] class = Not Popular 157->183 159 Artist_Followers ≤ 0.0 entropy = 0.68 samples = 50 value = [41, 9] class = Not Popular 158->159 178 Artist_Popularity ≤ 0.135 entropy = 0.996 samples = 13 value = [7, 6] class = Not Popular 158->178 160 Artist_Popularity ≤ 0.151 entropy = 0.759 samples = 41 value = [32, 9] class = Not Popular 159->160 177 entropy = 0.0 samples = 9 value = [9, 0] class = Not Popular 159->177 161 Artist_Followers ≤ 0.0 entropy = 0.689 samples = 38 value = [31, 7] class = Not Popular 160->161 176 entropy = 0.918 samples = 3 value = [1, 2] class = Popular 160->176 162 Artist_Followers ≤ 0.0 entropy = 0.391 samples = 13 value = [12, 1] class = Not Popular 161->162 165 Artist_Followers ≤ 0.0 entropy = 0.795 samples = 25 value = [19, 6] class = Not Popular 161->165 163 entropy = 0.503 samples = 9 value = [8, 1] class = Not Popular 162->163 164 entropy = 0.0 samples = 4 value = [4, 0] class = Not Popular 162->164 166 Artist_Popularity ≤ 0.127 entropy = 0.684 samples = 11 value = [9, 2] class = Not Popular 165->166 169 Artist_Followers ≤ 0.0 entropy = 0.863 samples = 14 value = [10, 4] class = Not Popular 165->169 167 entropy = 0.0 samples = 4 value = [4, 0] class = Not Popular 166->167 168 entropy = 0.863 samples = 7 value = [5, 2] class = Not Popular 166->168 170 Artist_Popularity ≤ 0.119 entropy = 0.918 samples = 6 value = [4, 2] class = Not Popular 169->170 173 Artist_Followers ≤ 0.0 entropy = 0.811 samples = 8 value = [6, 2] class = Not Popular 169->173 171 entropy = 0.811 samples = 4 value = [3, 1] class = Not Popular 170->171 172 entropy = 1.0 samples = 2 value = [1, 1] class = Not Popular 170->172 174 entropy = 0.0 samples = 1 value = [1, 0] class = Not Popular 173->174 175 entropy = 0.863 samples = 7 value = [5, 2] class = Not Popular 173->175 179 Artist_Followers ≤ 0.0 entropy = 0.985 samples = 7 value = [4, 3] class = Not Popular 178->179 182 entropy = 1.0 samples = 6 value = [3, 3] class = Not Popular 178->182 180 entropy = 1.0 samples = 2 value = [1, 1] class = Not Popular 179->180 181 entropy = 0.971 samples = 5 value = [3, 2] class = Not Popular 179->181 185 entropy = 0.0 samples = 10 value = [10, 0] class = Not Popular 184->185 186 Artist_Followers ≤ 0.0 entropy = 0.773 samples = 132 value = [102, 30] class = Not Popular 184->186 187 entropy = 0.0 samples = 2 value = [0, 2] class = Popular 186->187 188 Artist_Popularity ≤ 0.119 entropy = 0.752 samples = 130 value = [102, 28] class = Not Popular 186->188 189 Artist_Followers ≤ 0.0 entropy = 0.544 samples = 64 value = [56, 8] class = Not Popular 188->189 200 Artist_Followers ≤ 0.0 entropy = 0.885 samples = 66 value = [46, 20] class = Not Popular 188->200 190 entropy = 0.0 samples = 18 value = [18, 0] class = Not Popular 189->190 191 Artist_Followers ≤ 0.0 entropy = 0.667 samples = 46 value = [38, 8] class = Not Popular 189->191 192 Artist_Followers ≤ 0.0 entropy = 0.702 samples = 42 value = [34, 8] class = Not Popular 191->192 199 entropy = 0.0 samples = 4 value = [4, 0] class = Not Popular 191->199 193 entropy = 0.918 samples = 6 value = [4, 2] class = Not Popular 192->193 194 Artist_Followers ≤ 0.0 entropy = 0.65 samples = 36 value = [30, 6] class = Not Popular 192->194 195 entropy = 0.544 samples = 16 value = [14, 2] class = Not Popular 194->195 196 Artist_Popularity ≤ 0.095 entropy = 0.722 samples = 20 value = [16, 4] class = Not Popular 194->196 197 entropy = 0.722 samples = 10 value = [8, 2] class = Not Popular 196->197 198 entropy = 0.722 samples = 10 value = [8, 2] class = Not Popular 196->198 201 entropy = 0.722 samples = 5 value = [1, 4] class = Popular 200->201 202 Artist_Popularity ≤ 0.135 entropy = 0.83 samples = 61 value = [45, 16] class = Not Popular 200->202 203 Artist_Followers ≤ 0.0 entropy = 0.619 samples = 13 value = [11, 2] class = Not Popular 202->203 206 Artist_Followers ≤ 0.0 entropy = 0.871 samples = 48 value = [34, 14] class = Not Popular 202->206 204 entropy = 0.764 samples = 9 value = [7, 2] class = Not Popular 203->204 205 entropy = 0.0 samples = 4 value = [4, 0] class = Not Popular 203->205 207 entropy = 0.971 samples = 10 value = [6, 4] class = Not Popular 206->207 208 Artist_Followers ≤ 0.0 entropy = 0.831 samples = 38 value = [28, 10] class = Not Popular 206->208 209 Artist_Followers ≤ 0.0 entropy = 0.811 samples = 32 value = [24, 8] class = Not Popular 208->209 212 entropy = 0.918 samples = 6 value = [4, 2] class = Not Popular 208->212 210 entropy = 0.787 samples = 17 value = [13, 4] class = Not Popular 209->210 211 entropy = 0.837 samples = 15 value = [11, 4] class = Not Popular 209->211 214 Artist_Followers ≤ 0.001 entropy = 0.203 samples = 189 value = [183, 6] class = Not Popular 213->214 239 Artist_Followers ≤ 0.001 entropy = 0.503 samples = 1623 value = [1443, 180] class = Not Popular 213->239 215 entropy = 0.0 samples = 24 value = [24, 0] class = Not Popular 214->215 216 Artist_Followers ≤ 0.001 entropy = 0.225 samples = 165 value = [159, 6] class = Not Popular 214->216 217 entropy = 1.0 samples = 2 value = [1, 1] class = Not Popular 216->217 218 Artist_Followers ≤ 0.001 entropy = 0.198 samples = 163 value = [158, 5] class = Not Popular 216->218 219 entropy = 0.0 samples = 20 value = [20, 0] class = Not Popular 218->219 220 Artist_Followers ≤ 0.001 entropy = 0.219 samples = 143 value = [138, 5] class = Not Popular 218->220 221 Artist_Followers ≤ 0.001 entropy = 0.313 samples = 71 value = [67, 4] class = Not Popular 220->221 234 Artist_Followers ≤ 0.002 entropy = 0.106 samples = 72 value = [71, 1] class = Not Popular 220->234 222 Artist_Followers ≤ 0.001 entropy = 0.273 samples = 64 value = [61, 3] class = Not Popular 221->222 233 entropy = 0.592 samples = 7 value = [6, 1] class = Not Popular 221->233 223 Artist_Followers ≤ 0.001 entropy = 0.365 samples = 43 value = [40, 3] class = Not Popular 222->223 232 entropy = 0.0 samples = 21 value = [21, 0] class = Not Popular 222->232 224 Artist_Followers ≤ 0.001 entropy = 0.292 samples = 39 value = [37, 2] class = Not Popular 223->224 231 entropy = 0.811 samples = 4 value = [3, 1] class = Not Popular 223->231 225 Artist_Followers ≤ 0.001 entropy = 0.454 samples = 21 value = [19, 2] class = Not Popular 224->225 230 entropy = 0.0 samples = 18 value = [18, 0] class = Not Popular 224->230 226 Artist_Followers ≤ 0.001 entropy = 0.323 samples = 17 value = [16, 1] class = Not Popular 225->226 229 entropy = 0.811 samples = 4 value = [3, 1] class = Not Popular 225->229 227 entropy = 0.414 samples = 12 value = [11, 1] class = Not Popular 226->227 228 entropy = 0.0 samples = 5 value = [5, 0] class = Not Popular 226->228 235 entropy = 0.0 samples = 59 value = [59, 0] class = Not Popular 234->235 236 Artist_Followers ≤ 0.002 entropy = 0.391 samples = 13 value = [12, 1] class = Not Popular 234->236 237 entropy = 0.544 samples = 8 value = [7, 1] class = Not Popular 236->237 238 entropy = 0.0 samples = 5 value = [5, 0] class = Not Popular 236->238 240 Artist_Followers ≤ 0.001 entropy = 0.574 samples = 749 value = [647, 102] class = Not Popular 239->240 395 Artist_Followers ≤ 0.003 entropy = 0.434 samples = 874 value = [796, 78] class = Not Popular 239->395 241 Artist_Followers ≤ 0.0 entropy = 0.556 samples = 719 value = [626, 93] class = Not Popular 240->241 390 Artist_Followers ≤ 0.001 entropy = 0.881 samples = 30 value = [21, 9] class = Not Popular 240->390 242 entropy = 0.0 samples = 18 value = [18, 0] class = Not Popular 241->242 243 Artist_Followers ≤ 0.001 entropy = 0.565 samples = 701 value = [608, 93] class = Not Popular 241->243 244 Artist_Followers ≤ 0.001 entropy = 0.591 samples = 589 value = [505, 84] class = Not Popular 243->244 369 Artist_Popularity ≤ 0.071 entropy = 0.403 samples = 112 value = [103, 9] class = Not Popular 243->369 245 Artist_Followers ≤ 0.001 entropy = 0.55 samples = 424 value = [370, 54] class = Not Popular 244->245 338 Artist_Followers ≤ 0.001 entropy = 0.684 samples = 165 value = [135, 30] class = Not Popular 244->338 246 Artist_Followers ≤ 0.001 entropy = 0.577 samples = 379 value = [327, 52] class = Not Popular 245->246 333 Artist_Popularity ≤ 0.095 entropy = 0.262 samples = 45 value = [43, 2] class = Not Popular 245->333 247 Artist_Popularity ≤ 0.087 entropy = 0.539 samples = 349 value = [306, 43] class = Not Popular 246->247 326 Artist_Popularity ≤ 0.135 entropy = 0.881 samples = 30 value = [21, 9] class = Not Popular 246->326 248 Artist_Followers ≤ 0.0 entropy = 0.482 samples = 144 value = [129, 15] class = Not Popular 247->248 281 Artist_Followers ≤ 0.001 entropy = 0.575 samples = 205 value = [177, 28] class = Not Popular 247->281 249 entropy = 0.0 samples = 11 value = [11, 0] class = Not Popular 248->249 250 Artist_Followers ≤ 0.001 entropy = 0.508 samples = 133 value = [118, 15] class = Not Popular 248->250 251 Artist_Followers ≤ 0.001 entropy = 0.535 samples = 123 value = [108, 15] class = Not Popular 250->251 280 entropy = 0.0 samples = 10 value = [10, 0] class = Not Popular 250->280 252 Artist_Followers ≤ 0.001 entropy = 0.472 samples = 109 value = [98, 11] class = Not Popular 251->252 277 Artist_Popularity ≤ 0.063 entropy = 0.863 samples = 14 value = [10, 4] class = Not Popular 251->277 253 Artist_Followers ≤ 0.0 entropy = 0.624 samples = 45 value = [38, 7] class = Not Popular 252->253 264 Artist_Followers ≤ 0.001 entropy = 0.337 samples = 64 value = [60, 4] class = Not Popular 252->264 254 entropy = 0.811 samples = 8 value = [6, 2] class = Not Popular 253->254 255 Artist_Followers ≤ 0.001 entropy = 0.571 samples = 37 value = [32, 5] class = Not Popular 253->255 256 Artist_Popularity ≤ 0.063 entropy = 0.371 samples = 14 value = [13, 1] class = Not Popular 255->256 259 Artist_Popularity ≤ 0.071 entropy = 0.667 samples = 23 value = [19, 4] class = Not Popular 255->259 257 entropy = 0.503 samples = 9 value = [8, 1] class = Not Popular 256->257 258 entropy = 0.0 samples = 5 value = [5, 0] class = Not Popular 256->258 260 Artist_Popularity ≤ 0.056 entropy = 0.567 samples = 15 value = [13, 2] class = Not Popular 259->260 263 entropy = 0.811 samples = 8 value = [6, 2] class = Not Popular 259->263 261 entropy = 0.722 samples = 5 value = [4, 1] class = Not Popular 260->261 262 entropy = 0.469 samples = 10 value = [9, 1] class = Not Popular 260->262 265 entropy = 0.0 samples = 13 value = [13, 0] class = Not Popular 264->265 266 Artist_Popularity ≤ 0.071 entropy = 0.397 samples = 51 value = [47, 4] class = Not Popular 264->266 267 Artist_Followers ≤ 0.001 entropy = 0.446 samples = 43 value = [39, 4] class = Not Popular 266->267 276 entropy = 0.0 samples = 8 value = [8, 0] class = Not Popular 266->276 268 entropy = 0.65 samples = 6 value = [5, 1] class = Not Popular 267->268 269 Artist_Popularity ≤ 0.056 entropy = 0.406 samples = 37 value = [34, 3] class = Not Popular 267->269 270 entropy = 0.0 samples = 1 value = [1, 0] class = Not Popular 269->270 271 Artist_Followers ≤ 0.001 entropy = 0.414 samples = 36 value = [33, 3] class = Not Popular 269->271 272 entropy = 0.414 samples = 12 value = [11, 1] class = Not Popular 271->272 273 Artist_Followers ≤ 0.001 entropy = 0.414 samples = 24 value = [22, 2] class = Not Popular 271->273 274 entropy = 0.414 samples = 12 value = [11, 1] class = Not Popular 273->274 275 entropy = 0.414 samples = 12 value = [11, 1] class = Not Popular 273->275 278 entropy = 0.918 samples = 6 value = [4, 2] class = Not Popular 277->278 279 entropy = 0.811 samples = 8 value = [6, 2] class = Not Popular 277->279 282 Artist_Followers ≤ 0.001 entropy = 0.664 samples = 81 value = [67, 14] class = Not Popular 281->282 303 Artist_Followers ≤ 0.001 entropy = 0.509 samples = 124 value = [110, 14] class = Not Popular 281->303 283 Artist_Followers ≤ 0.0 entropy = 0.514 samples = 61 value = [54, 7] class = Not Popular 282->283 296 Artist_Popularity ≤ 0.111 entropy = 0.934 samples = 20 value = [13, 7] class = Not Popular 282->296 284 Artist_Popularity ≤ 0.103 entropy = 0.615 samples = 46 value = [39, 7] class = Not Popular 283->284 295 entropy = 0.0 samples = 15 value = [15, 0] class = Not Popular 283->295 285 Artist_Followers ≤ 0.0 entropy = 0.469 samples = 10 value = [9, 1] class = Not Popular 284->285 288 Artist_Popularity ≤ 0.151 entropy = 0.65 samples = 36 value = [30, 6] class = Not Popular 284->288 286 entropy = 0.544 samples = 8 value = [7, 1] class = Not Popular 285->286 287 entropy = 0.0 samples = 2 value = [2, 0] class = Not Popular 285->287 289 Artist_Followers ≤ 0.0 entropy = 0.619 samples = 26 value = [22, 4] class = Not Popular 288->289 294 entropy = 0.722 samples = 10 value = [8, 2] class = Not Popular 288->294 290 Artist_Followers ≤ 0.0 entropy = 0.65 samples = 12 value = [10, 2] class = Not Popular 289->290 293 entropy = 0.592 samples = 14 value = [12, 2] class = Not Popular 289->293 291 entropy = 0.65 samples = 6 value = [5, 1] class = Not Popular 290->291 292 entropy = 0.65 samples = 6 value = [5, 1] class = Not Popular 290->292 297 entropy = 0.0 samples = 3 value = [0, 3] class = Popular 296->297 298 Artist_Popularity ≤ 0.135 entropy = 0.787 samples = 17 value = [13, 4] class = Not Popular 296->298 299 Artist_Followers ≤ 0.001 entropy = 0.918 samples = 12 value = [8, 4] class = Not Popular 298->299 302 entropy = 0.0 samples = 5 value = [5, 0] class = Not Popular 298->302 300 entropy = 0.971 samples = 5 value = [3, 2] class = Not Popular 299->300 301 entropy = 0.863 samples = 7 value = [5, 2] class = Not Popular 299->301 304 entropy = 0.0 samples = 15 value = [15, 0] class = Not Popular 303->304 305 Artist_Followers ≤ 0.001 entropy = 0.553 samples = 109 value = [95, 14] class = Not Popular 303->305 306 Artist_Followers ≤ 0.001 entropy = 0.353 samples = 15 value = [14, 1] class = Not Popular 305->306 309 Artist_Followers ≤ 0.001 entropy = 0.58 samples = 94 value = [81, 13] class = Not Popular 305->309 307 entropy = 0.544 samples = 8 value = [7, 1] class = Not Popular 306->307 308 entropy = 0.0 samples = 7 value = [7, 0] class = Not Popular 306->308 310 Artist_Followers ≤ 0.001 entropy = 0.663 samples = 29 value = [24, 5] class = Not Popular 309->310 315 Artist_Followers ≤ 0.001 entropy = 0.538 samples = 65 value = [57, 8] class = Not Popular 309->315 311 Artist_Followers ≤ 0.001 entropy = 0.544 samples = 16 value = [14, 2] class = Not Popular 310->311 314 entropy = 0.779 samples = 13 value = [10, 3] class = Not Popular 310->314 312 entropy = 0.619 samples = 13 value = [11, 2] class = Not Popular 311->312 313 entropy = 0.0 samples = 3 value = [3, 0] class = Not Popular 311->313 316 entropy = 0.0 samples = 9 value = [9, 0] class = Not Popular 315->316 317 Artist_Followers ≤ 0.001 entropy = 0.592 samples = 56 value = [48, 8] class = Not Popular 315->317 318 Artist_Popularity ≤ 0.119 entropy = 0.663 samples = 29 value = [24, 5] class = Not Popular 317->318 321 Artist_Followers ≤ 0.001 entropy = 0.503 samples = 27 value = [24, 3] class = Not Popular 317->321 319 entropy = 0.722 samples = 10 value = [8, 2] class = Not Popular 318->319 320 entropy = 0.629 samples = 19 value = [16, 3] class = Not Popular 318->320 322 entropy = 0.0 samples = 2 value = [2, 0] class = Not Popular 321->322 323 Artist_Popularity ≤ 0.143 entropy = 0.529 samples = 25 value = [22, 3] class = Not Popular 321->323 324 entropy = 0.503 samples = 9 value = [8, 1] class = Not Popular 323->324 325 entropy = 0.544 samples = 16 value = [14, 2] class = Not Popular 323->325 327 Artist_Popularity ≤ 0.103 entropy = 0.918 samples = 12 value = [8, 4] class = Not Popular 326->327 330 Artist_Popularity ≤ 0.151 entropy = 0.852 samples = 18 value = [13, 5] class = Not Popular 326->330 328 entropy = 0.918 samples = 9 value = [6, 3] class = Not Popular 327->328 329 entropy = 0.918 samples = 3 value = [2, 1] class = Not Popular 327->329 331 entropy = 0.863 samples = 7 value = [5, 2] class = Not Popular 330->331 332 entropy = 0.845 samples = 11 value = [8, 3] class = Not Popular 330->332 334 entropy = 0.0 samples = 17 value = [17, 0] class = Not Popular 333->334 335 Artist_Followers ≤ 0.001 entropy = 0.371 samples = 28 value = [26, 2] class = Not Popular 333->335 336 entropy = 0.337 samples = 16 value = [15, 1] class = Not Popular 335->336 337 entropy = 0.414 samples = 12 value = [11, 1] class = Not Popular 335->337 339 Artist_Popularity ≤ 0.111 entropy = 0.878 samples = 37 value = [26, 11] class = Not Popular 338->339 346 Artist_Followers ≤ 0.001 entropy = 0.606 samples = 128 value = [109, 19] class = Not Popular 338->346 340 Artist_Popularity ≤ 0.087 entropy = 0.98 samples = 12 value = [7, 5] class = Not Popular 339->340 343 Artist_Followers ≤ 0.001 entropy = 0.795 samples = 25 value = [19, 6] class = Not Popular 339->343 341 entropy = 1.0 samples = 4 value = [2, 2] class = Not Popular 340->341 342 entropy = 0.954 samples = 8 value = [5, 3] class = Not Popular 340->342 344 entropy = 0.811 samples = 12 value = [9, 3] class = Not Popular 343->344 345 entropy = 0.779 samples = 13 value = [10, 3] class = Not Popular 343->345 347 Artist_Followers ≤ 0.001 entropy = 0.276 samples = 42 value = [40, 2] class = Not Popular 346->347 354 Artist_Followers ≤ 0.001 entropy = 0.717 samples = 86 value = [69, 17] class = Not Popular 346->354 348 Artist_Popularity ≤ 0.087 entropy = 0.426 samples = 23 value = [21, 2] class = Not Popular 347->348 353 entropy = 0.0 samples = 19 value = [19, 0] class = Not Popular 347->353 349 Artist_Followers ≤ 0.001 entropy = 0.469 samples = 20 value = [18, 2] class = Not Popular 348->349 352 entropy = 0.0 samples = 3 value = [3, 0] class = Not Popular 348->352 350 entropy = 0.371 samples = 14 value = [13, 1] class = Not Popular 349->350 351 entropy = 0.65 samples = 6 value = [5, 1] class = Not Popular 349->351 355 entropy = 0.811 samples = 4 value = [1, 3] class = Popular 354->355 356 Artist_Popularity ≤ 0.103 entropy = 0.659 samples = 82 value = [68, 14] class = Not Popular 354->356 357 Artist_Followers ≤ 0.001 entropy = 0.348 samples = 46 value = [43, 3] class = Not Popular 356->357 362 Artist_Followers ≤ 0.001 entropy = 0.888 samples = 36 value = [25, 11] class = Not Popular 356->362 358 Artist_Popularity ≤ 0.087 entropy = 0.459 samples = 31 value = [28, 3] class = Not Popular 357->358 361 entropy = 0.0 samples = 15 value = [15, 0] class = Not Popular 357->361 359 entropy = 0.469 samples = 10 value = [9, 1] class = Not Popular 358->359 360 entropy = 0.454 samples = 21 value = [19, 2] class = Not Popular 358->360 363 entropy = 1.0 samples = 8 value = [4, 4] class = Not Popular 362->363 364 Artist_Popularity ≤ 0.119 entropy = 0.811 samples = 28 value = [21, 7] class = Not Popular 362->364 365 entropy = 0.918 samples = 3 value = [2, 1] class = Not Popular 364->365 366 Artist_Popularity ≤ 0.143 entropy = 0.795 samples = 25 value = [19, 6] class = Not Popular 364->366 367 entropy = 0.811 samples = 12 value = [9, 3] class = Not Popular 366->367 368 entropy = 0.779 samples = 13 value = [10, 3] class = Not Popular 366->368 370 entropy = 0.0 samples = 19 value = [19, 0] class = Not Popular 369->370 371 Artist_Followers ≤ 0.001 entropy = 0.459 samples = 93 value = [84, 9] class = Not Popular 369->371 372 entropy = 0.0 samples = 9 value = [9, 0] class = Not Popular 371->372 373 Artist_Followers ≤ 0.001 entropy = 0.491 samples = 84 value = [75, 9] class = Not Popular 371->373 374 Artist_Followers ≤ 0.001 entropy = 0.408 samples = 49 value = [45, 4] class = Not Popular 373->374 385 Artist_Popularity ≤ 0.087 entropy = 0.592 samples = 35 value = [30, 5] class = Not Popular 373->385 375 Artist_Popularity ≤ 0.135 entropy = 0.433 samples = 45 value = [41, 4] class = Not Popular 374->375 384 entropy = 0.0 samples = 4 value = [4, 0] class = Not Popular 374->384 376 Artist_Followers ≤ 0.001 entropy = 0.454 samples = 42 value = [38, 4] class = Not Popular 375->376 383 entropy = 0.0 samples = 3 value = [3, 0] class = Not Popular 375->383 377 entropy = 0.503 samples = 9 value = [8, 1] class = Not Popular 376->377 378 Artist_Followers ≤ 0.001 entropy = 0.439 samples = 33 value = [30, 3] class = Not Popular 376->378 379 entropy = 0.0 samples = 3 value = [3, 0] class = Not Popular 378->379 380 Artist_Popularity ≤ 0.111 entropy = 0.469 samples = 30 value = [27, 3] class = Not Popular 378->380 381 entropy = 0.454 samples = 21 value = [19, 2] class = Not Popular 380->381 382 entropy = 0.503 samples = 9 value = [8, 1] class = Not Popular 380->382 386 entropy = 0.414 samples = 12 value = [11, 1] class = Not Popular 385->386 387 Artist_Popularity ≤ 0.111 entropy = 0.667 samples = 23 value = [19, 4] class = Not Popular 385->387 388 entropy = 0.65 samples = 6 value = [5, 1] class = Not Popular 387->388 389 entropy = 0.672 samples = 17 value = [14, 3] class = Not Popular 387->389 391 Artist_Followers ≤ 0.001 entropy = 0.831 samples = 19 value = [14, 5] class = Not Popular 390->391 394 entropy = 0.946 samples = 11 value = [7, 4] class = Not Popular 390->394 392 entropy = 0.896 samples = 16 value = [11, 5] class = Not Popular 391->392 393 entropy = 0.0 samples = 3 value = [3, 0] class = Not Popular 391->393 396 Artist_Popularity ≤ 0.087 entropy = 0.393 samples = 697 value = [643, 54] class = Not Popular 395->396 473 Artist_Popularity ≤ 0.087 entropy = 0.573 samples = 177 value = [153, 24] class = Not Popular 395->473 397 Artist_Followers ≤ 0.002 entropy = 0.087 samples = 92 value = [91, 1] class = Not Popular 396->397 402 Artist_Followers ≤ 0.002 entropy = 0.428 samples = 605 value = [552, 53] class = Not Popular 396->402 398 entropy = 0.0 samples = 71 value = [71, 0] class = Not Popular 397->398 399 Artist_Followers ≤ 0.002 entropy = 0.276 samples = 21 value = [20, 1] class = Not Popular 397->399 400 entropy = 0.503 samples = 9 value = [8, 1] class = Not Popular 399->400 401 entropy = 0.0 samples = 12 value = [12, 0] class = Not Popular 399->401 403 Artist_Followers ≤ 0.002 entropy = 0.523 samples = 340 value = [300, 40] class = Not Popular 402->403 452 Artist_Followers ≤ 0.002 entropy = 0.282 samples = 265 value = [252, 13] class = Not Popular 402->452 404 Artist_Followers ≤ 0.002 entropy = 0.458 samples = 280 value = [253, 27] class = Not Popular 403->404 445 Artist_Followers ≤ 0.002 entropy = 0.754 samples = 60 value = [47, 13] class = Not Popular 403->445 405 Artist_Followers ≤ 0.002 entropy = 0.48 samples = 261 value = [234, 27] class = Not Popular 404->405 444 entropy = 0.0 samples = 19 value = [19, 0] class = Not Popular 404->444 406 Artist_Followers ≤ 0.001 entropy = 0.406 samples = 197 value = [181, 16] class = Not Popular 405->406 433 Artist_Followers ≤ 0.002 entropy = 0.662 samples = 64 value = [53, 11] class = Not Popular 405->433 407 Artist_Followers ≤ 0.001 entropy = 0.525 samples = 76 value = [67, 9] class = Not Popular 406->407 420 Artist_Followers ≤ 0.001 entropy = 0.319 samples = 121 value = [114, 7] class = Not Popular 406->420 408 entropy = 0.0 samples = 9 value = [9, 0] class = Not Popular 407->408 409 Artist_Popularity ≤ 0.135 entropy = 0.569 samples = 67 value = [58, 9] class = Not Popular 407->409 410 Artist_Followers ≤ 0.001 entropy = 0.509 samples = 62 value = [55, 7] class = Not Popular 409->410 419 entropy = 0.971 samples = 5 value = [3, 2] class = Not Popular 409->419 411 Artist_Followers ≤ 0.001 entropy = 0.378 samples = 41 value = [38, 3] class = Not Popular 410->411 416 Artist_Followers ≤ 0.001 entropy = 0.702 samples = 21 value = [17, 4] class = Not Popular 410->416 412 Artist_Followers ≤ 0.001 entropy = 0.469 samples = 20 value = [18, 2] class = Not Popular 411->412 415 entropy = 0.276 samples = 21 value = [20, 1] class = Not Popular 411->415 413 entropy = 0.469 samples = 10 value = [9, 1] class = Not Popular 412->413 414 entropy = 0.469 samples = 10 value = [9, 1] class = Not Popular 412->414 417 entropy = 0.863 samples = 7 value = [5, 2] class = Not Popular 416->417 418 entropy = 0.592 samples = 14 value = [12, 2] class = Not Popular 416->418 421 Artist_Popularity ≤ 0.127 entropy = 0.172 samples = 39 value = [38, 1] class = Not Popular 420->421 424 Artist_Followers ≤ 0.001 entropy = 0.378 samples = 82 value = [76, 6] class = Not Popular 420->424 422 entropy = 0.0 samples = 9 value = [9, 0] class = Not Popular 421->422 423 entropy = 0.211 samples = 30 value = [29, 1] class = Not Popular 421->423 425 Artist_Followers ≤ 0.001 entropy = 0.592 samples = 21 value = [18, 3] class = Not Popular 424->425 428 Artist_Followers ≤ 0.002 entropy = 0.283 samples = 61 value = [58, 3] class = Not Popular 424->428 426 entropy = 0.592 samples = 7 value = [6, 1] class = Not Popular 425->426 427 entropy = 0.592 samples = 14 value = [12, 2] class = Not Popular 425->427 429 entropy = 0.0 samples = 10 value = [10, 0] class = Not Popular 428->429 430 Artist_Popularity ≤ 0.151 entropy = 0.323 samples = 51 value = [48, 3] class = Not Popular 428->430 431 entropy = 0.381 samples = 27 value = [25, 2] class = Not Popular 430->431 432 entropy = 0.25 samples = 24 value = [23, 1] class = Not Popular 430->432 434 Artist_Followers ≤ 0.002 entropy = 0.792 samples = 42 value = [32, 10] class = Not Popular 433->434 443 entropy = 0.267 samples = 22 value = [21, 1] class = Not Popular 433->443 435 Artist_Followers ≤ 0.002 entropy = 0.679 samples = 39 value = [32, 7] class = Not Popular 434->435 442 entropy = 0.0 samples = 3 value = [0, 3] class = Popular 434->442 436 Artist_Followers ≤ 0.002 entropy = 0.619 samples = 26 value = [22, 4] class = Not Popular 435->436 439 Artist_Popularity ≤ 0.127 entropy = 0.779 samples = 13 value = [10, 3] class = Not Popular 435->439 437 entropy = 0.811 samples = 8 value = [6, 2] class = Not Popular 436->437 438 entropy = 0.503 samples = 18 value = [16, 2] class = Not Popular 436->438 440 entropy = 0.811 samples = 8 value = [6, 2] class = Not Popular 439->440 441 entropy = 0.722 samples = 5 value = [4, 1] class = Not Popular 439->441 446 Artist_Followers ≤ 0.002 entropy = 0.714 samples = 51 value = [41, 10] class = Not Popular 445->446 451 entropy = 0.918 samples = 9 value = [6, 3] class = Not Popular 445->451 447 Artist_Popularity ≤ 0.135 entropy = 0.776 samples = 35 value = [27, 8] class = Not Popular 446->447 450 entropy = 0.544 samples = 16 value = [14, 2] class = Not Popular 446->450 448 entropy = 0.845 samples = 11 value = [8, 3] class = Not Popular 447->448 449 entropy = 0.738 samples = 24 value = [19, 5] class = Not Popular 447->449 453 entropy = 0.0 samples = 66 value = [66, 0] class = Not Popular 452->453 454 Artist_Followers ≤ 0.003 entropy = 0.348 samples = 199 value = [186, 13] class = Not Popular 452->454 455 entropy = 0.863 samples = 7 value = [5, 2] class = Not Popular 454->455 456 Artist_Followers ≤ 0.003 entropy = 0.317 samples = 192 value = [181, 11] class = Not Popular 454->456 457 Artist_Followers ≤ 0.003 entropy = 0.341 samples = 142 value = [133, 9] class = Not Popular 456->457 470 Artist_Popularity ≤ 0.135 entropy = 0.242 samples = 50 value = [48, 2] class = Not Popular 456->470 458 Artist_Followers ≤ 0.003 entropy = 0.311 samples = 125 value = [118, 7] class = Not Popular 457->458 467 Artist_Popularity ≤ 0.127 entropy = 0.523 samples = 17 value = [15, 2] class = Not Popular 457->467 459 Artist_Followers ≤ 0.003 entropy = 0.357 samples = 74 value = [69, 5] class = Not Popular 458->459 464 Artist_Followers ≤ 0.003 entropy = 0.239 samples = 51 value = [49, 2] class = Not Popular 458->464 460 entropy = 0.426 samples = 23 value = [21, 2] class = Not Popular 459->460 461 Artist_Popularity ≤ 0.127 entropy = 0.323 samples = 51 value = [48, 3] class = Not Popular 459->461 462 entropy = 0.353 samples = 15 value = [14, 1] class = Not Popular 461->462 463 entropy = 0.31 samples = 36 value = [34, 2] class = Not Popular 461->463 465 entropy = 0.206 samples = 31 value = [30, 1] class = Not Popular 464->465 466 entropy = 0.286 samples = 20 value = [19, 1] class = Not Popular 464->466 468 entropy = 0.544 samples = 8 value = [7, 1] class = Not Popular 467->468 469 entropy = 0.503 samples = 9 value = [8, 1] class = Not Popular 467->469 471 entropy = 0.235 samples = 26 value = [25, 1] class = Not Popular 470->471 472 entropy = 0.25 samples = 24 value = [23, 1] class = Not Popular 470->472 474 entropy = 0.0 samples = 3 value = [0, 3] class = Popular 473->474 475 Artist_Followers ≤ 0.008 entropy = 0.531 samples = 174 value = [153, 21] class = Not Popular 473->475 476 Artist_Followers ≤ 0.005 entropy = 0.562 samples = 152 value = [132, 20] class = Not Popular 475->476 499 entropy = 0.267 samples = 22 value = [21, 1] class = Not Popular 475->499 477 Artist_Followers ≤ 0.004 entropy = 0.501 samples = 145 value = [129, 16] class = Not Popular 476->477 496 Artist_Followers ≤ 0.006 entropy = 0.985 samples = 7 value = [3, 4] class = Popular 476->496 478 Artist_Popularity ≤ 0.151 entropy = 0.592 samples = 98 value = [84, 14] class = Not Popular 477->478 491 Artist_Popularity ≤ 0.135 entropy = 0.254 samples = 47 value = [45, 2] class = Not Popular 477->491 479 Artist_Popularity ≤ 0.119 entropy = 0.567 samples = 75 value = [65, 10] class = Not Popular 478->479 488 Artist_Followers ≤ 0.004 entropy = 0.667 samples = 23 value = [19, 4] class = Not Popular 478->488 480 Artist_Followers ≤ 0.003 entropy = 0.65 samples = 24 value = [20, 4] class = Not Popular 479->480 483 Artist_Followers ≤ 0.004 entropy = 0.523 samples = 51 value = [45, 6] class = Not Popular 479->483 481 entropy = 0.619 samples = 13 value = [11, 2] class = Not Popular 480->481 482 entropy = 0.684 samples = 11 value = [9, 2] class = Not Popular 480->482 484 Artist_Followers ≤ 0.004 entropy = 0.469 samples = 40 value = [36, 4] class = Not Popular 483->484 487 entropy = 0.684 samples = 11 value = [9, 2] class = Not Popular 483->487 485 entropy = 0.544 samples = 24 value = [21, 3] class = Not Popular 484->485 486 entropy = 0.337 samples = 16 value = [15, 1] class = Not Popular 484->486 489 entropy = 0.722 samples = 10 value = [8, 2] class = Not Popular 488->489 490 entropy = 0.619 samples = 13 value = [11, 2] class = Not Popular 488->490 492 Artist_Followers ≤ 0.005 entropy = 0.353 samples = 30 value = [28, 2] class = Not Popular 491->492 495 entropy = 0.0 samples = 17 value = [17, 0] class = Not Popular 491->495 493 entropy = 0.391 samples = 13 value = [12, 1] class = Not Popular 492->493 494 entropy = 0.323 samples = 17 value = [16, 1] class = Not Popular 492->494 497 entropy = 0.0 samples = 2 value = [0, 2] class = Popular 496->497 498 entropy = 0.971 samples = 5 value = [3, 2] class = Not Popular 496->498 501 Artist_Popularity ≤ 0.246 entropy = 0.802 samples = 2488 value = [1880, 608] class = Not Popular 500->501 832 Artist_Followers ≤ 0.024 entropy = 0.647 samples = 3215 value = [2683, 532] class = Not Popular 500->832 502 Artist_Popularity ≤ 0.23 entropy = 0.732 samples = 1229 value = [977, 252] class = Not Popular 501->502 691 Artist_Followers ≤ 0.001 entropy = 0.859 samples = 1259 value = [903, 356] class = Not Popular 501->691 503 Artist_Popularity ≤ 0.214 entropy = 0.764 samples = 964 value = [750, 214] class = Not Popular 502->503 656 Artist_Followers ≤ 0.001 entropy = 0.593 samples = 265 value = [227, 38] class = Not Popular 502->656 504 Artist_Followers ≤ 0.0 entropy = 0.725 samples = 759 value = [606, 153] class = Not Popular 503->504 621 Artist_Followers ≤ 0.002 entropy = 0.878 samples = 205 value = [144, 61] class = Not Popular 503->621 505 Artist_Followers ≤ 0.0 entropy = 0.989 samples = 16 value = [9, 7] class = Not Popular 504->505 510 Artist_Followers ≤ 0.0 entropy = 0.715 samples = 743 value = [597, 146] class = Not Popular 504->510 506 entropy = 0.0 samples = 1 value = [0, 1] class = Popular 505->506 507 Artist_Popularity ≤ 0.183 entropy = 0.971 samples = 15 value = [9, 6] class = Not Popular 505->507 508 entropy = 0.971 samples = 5 value = [3, 2] class = Not Popular 507->508 509 entropy = 0.971 samples = 10 value = [6, 4] class = Not Popular 507->509 511 entropy = 0.0 samples = 16 value = [16, 0] class = Not Popular 510->511 512 Artist_Followers ≤ 0.001 entropy = 0.724 samples = 727 value = [581, 146] class = Not Popular 510->512 513 Artist_Followers ≤ 0.0 entropy = 0.644 samples = 195 value = [163, 32] class = Not Popular 512->513 548 Artist_Followers ≤ 0.001 entropy = 0.75 samples = 532 value = [418, 114] class = Not Popular 512->548 514 Artist_Followers ≤ 0.0 entropy = 0.779 samples = 91 value = [70, 21] class = Not Popular 513->514 535 Artist_Followers ≤ 0.001 entropy = 0.487 samples = 104 value = [93, 11] class = Not Popular 513->535 515 Artist_Popularity ≤ 0.183 entropy = 1.0 samples = 4 value = [2, 2] class = Not Popular 514->515 518 Artist_Followers ≤ 0.0 entropy = 0.757 samples = 87 value = [68, 19] class = Not Popular 514->518 516 entropy = 0.0 samples = 1 value = [0, 1] class = Popular 515->516 517 entropy = 0.918 samples = 3 value = [2, 1] class = Not Popular 515->517 519 Artist_Popularity ≤ 0.19 entropy = 0.691 samples = 27 value = [22, 5] class = Not Popular 518->519 522 Artist_Popularity ≤ 0.198 entropy = 0.784 samples = 60 value = [46, 14] class = Not Popular 518->522 520 entropy = 0.918 samples = 6 value = [4, 2] class = Not Popular 519->520 521 entropy = 0.592 samples = 21 value = [18, 3] class = Not Popular 519->521 523 Artist_Followers ≤ 0.0 entropy = 0.747 samples = 47 value = [37, 10] class = Not Popular 522->523 534 entropy = 0.89 samples = 13 value = [9, 4] class = Not Popular 522->534 524 Artist_Popularity ≤ 0.183 entropy = 0.759 samples = 41 value = [32, 9] class = Not Popular 523->524 531 Artist_Followers ≤ 0.0 entropy = 0.65 samples = 6 value = [5, 1] class = Not Popular 523->531 525 Artist_Followers ≤ 0.0 entropy = 0.742 samples = 19 value = [15, 4] class = Not Popular 524->525 528 Artist_Followers ≤ 0.0 entropy = 0.773 samples = 22 value = [17, 5] class = Not Popular 524->528 526 entropy = 0.811 samples = 4 value = [3, 1] class = Not Popular 525->526 527 entropy = 0.722 samples = 15 value = [12, 3] class = Not Popular 525->527 529 entropy = 0.764 samples = 9 value = [7, 2] class = Not Popular 528->529 530 entropy = 0.779 samples = 13 value = [10, 3] class = Not Popular 528->530 532 entropy = 0.0 samples = 2 value = [2, 0] class = Not Popular 531->532 533 entropy = 0.811 samples = 4 value = [3, 1] class = Not Popular 531->533 536 Artist_Popularity ≤ 0.198 entropy = 0.225 samples = 55 value = [53, 2] class = Not Popular 535->536 539 Artist_Followers ≤ 0.001 entropy = 0.688 samples = 49 value = [40, 9] class = Not Popular 535->539 537 entropy = 0.0 samples = 44 value = [44, 0] class = Not Popular 536->537 538 entropy = 0.684 samples = 11 value = [9, 2] class = Not Popular 536->538 540 Artist_Followers ≤ 0.001 entropy = 0.764 samples = 36 value = [28, 8] class = Not Popular 539->540 547 entropy = 0.391 samples = 13 value = [12, 1] class = Not Popular 539->547 541 entropy = 0.971 samples = 5 value = [3, 2] class = Not Popular 540->541 542 Artist_Followers ≤ 0.001 entropy = 0.709 samples = 31 value = [25, 6] class = Not Popular 540->542 543 entropy = 0.0 samples = 6 value = [6, 0] class = Not Popular 542->543 544 Artist_Followers ≤ 0.001 entropy = 0.795 samples = 25 value = [19, 6] class = Not Popular 542->544 545 entropy = 0.831 samples = 19 value = [14, 5] class = Not Popular 544->545 546 entropy = 0.65 samples = 6 value = [5, 1] class = Not Popular 544->546 549 entropy = 0.918 samples = 6 value = [2, 4] class = Popular 548->549 550 Artist_Followers ≤ 0.002 entropy = 0.74 samples = 526 value = [416, 110] class = Not Popular 548->550 551 Artist_Popularity ≤ 0.183 entropy = 0.773 samples = 343 value = [265, 78] class = Not Popular 550->551 598 Artist_Followers ≤ 0.002 entropy = 0.669 samples = 183 value = [151, 32] class = Not Popular 550->598 552 Artist_Followers ≤ 0.002 entropy = 0.656 samples = 130 value = [108, 22] class = Not Popular 551->552 571 Artist_Followers ≤ 0.001 entropy = 0.831 samples = 213 value = [157, 56] class = Not Popular 551->571 553 Artist_Followers ≤ 0.001 entropy = 0.588 samples = 106 value = [91, 15] class = Not Popular 552->553 568 Artist_Followers ≤ 0.002 entropy = 0.871 samples = 24 value = [17, 7] class = Not Popular 552->568 554 Artist_Followers ≤ 0.001 entropy = 0.629 samples = 95 value = [80, 15] class = Not Popular 553->554 567 entropy = 0.0 samples = 11 value = [11, 0] class = Not Popular 553->567 555 Artist_Followers ≤ 0.001 entropy = 0.75 samples = 14 value = [11, 3] class = Not Popular 554->555 558 Artist_Followers ≤ 0.001 entropy = 0.605 samples = 81 value = [69, 12] class = Not Popular 554->558 556 entropy = 0.544 samples = 8 value = [7, 1] class = Not Popular 555->556 557 entropy = 0.918 samples = 6 value = [4, 2] class = Not Popular 555->557 559 Artist_Followers ≤ 0.001 entropy = 0.58 samples = 65 value = [56, 9] class = Not Popular 558->559 566 entropy = 0.696 samples = 16 value = [13, 3] class = Not Popular 558->566 560 Artist_Followers ≤ 0.001 entropy = 0.612 samples = 53 value = [45, 8] class = Not Popular 559->560 563 Artist_Followers ≤ 0.001 entropy = 0.414 samples = 12 value = [11, 1] class = Not Popular 559->563 561 entropy = 0.65 samples = 18 value = [15, 3] class = Not Popular 560->561 562 entropy = 0.592 samples = 35 value = [30, 5] class = Not Popular 560->562 564 entropy = 0.0 samples = 4 value = [4, 0] class = Not Popular 563->564 565 entropy = 0.544 samples = 8 value = [7, 1] class = Not Popular 563->565 569 entropy = 0.811 samples = 12 value = [9, 3] class = Not Popular 568->569 570 entropy = 0.918 samples = 12 value = [8, 4] class = Not Popular 568->570 572 entropy = 0.503 samples = 9 value = [8, 1] class = Not Popular 571->572 573 Artist_Followers ≤ 0.002 entropy = 0.841 samples = 204 value = [149, 55] class = Not Popular 571->573 574 Artist_Followers ≤ 0.001 entropy = 0.867 samples = 163 value = [116, 47] class = Not Popular 573->574 595 Artist_Followers ≤ 0.002 entropy = 0.712 samples = 41 value = [33, 8] class = Not Popular 573->595 575 Artist_Followers ≤ 0.001 entropy = 0.836 samples = 139 value = [102, 37] class = Not Popular 574->575 594 entropy = 0.98 samples = 24 value = [14, 10] class = Not Popular 574->594 576 Artist_Followers ≤ 0.001 entropy = 0.863 samples = 112 value = [80, 32] class = Not Popular 575->576 589 Artist_Followers ≤ 0.001 entropy = 0.691 samples = 27 value = [22, 5] class = Not Popular 575->589 577 Artist_Popularity ≤ 0.198 entropy = 0.811 samples = 60 value = [45, 15] class = Not Popular 576->577 582 Artist_Followers ≤ 0.001 entropy = 0.912 samples = 52 value = [35, 17] class = Not Popular 576->582 578 Artist_Followers ≤ 0.001 entropy = 0.839 samples = 41 value = [30, 11] class = Not Popular 577->578 581 entropy = 0.742 samples = 19 value = [15, 4] class = Not Popular 577->581 579 entropy = 0.742 samples = 19 value = [15, 4] class = Not Popular 578->579 580 entropy = 0.902 samples = 22 value = [15, 7] class = Not Popular 578->580 583 entropy = 0.65 samples = 6 value = [1, 5] class = Popular 582->583 584 Artist_Followers ≤ 0.001 entropy = 0.828 samples = 46 value = [34, 12] class = Not Popular 582->584 585 entropy = 0.414 samples = 12 value = [11, 1] class = Not Popular 584->585 586 Artist_Popularity ≤ 0.198 entropy = 0.908 samples = 34 value = [23, 11] class = Not Popular 584->586 587 entropy = 0.937 samples = 17 value = [11, 6] class = Not Popular 586->587 588 entropy = 0.874 samples = 17 value = [12, 5] class = Not Popular 586->588 590 Artist_Followers ≤ 0.001 entropy = 0.706 samples = 26 value = [21, 5] class = Not Popular 589->590 593 entropy = 0.0 samples = 1 value = [1, 0] class = Not Popular 589->593 591 entropy = 0.702 samples = 21 value = [17, 4] class = Not Popular 590->591 592 entropy = 0.722 samples = 5 value = [4, 1] class = Not Popular 590->592 596 entropy = 0.672 samples = 17 value = [14, 3] class = Not Popular 595->596 597 entropy = 0.738 samples = 24 value = [19, 5] class = Not Popular 595->597 599 Artist_Followers ≤ 0.002 entropy = 0.4 samples = 63 value = [58, 5] class = Not Popular 598->599 608 Artist_Followers ≤ 0.002 entropy = 0.769 samples = 120 value = [93, 27] class = Not Popular 598->608 600 Artist_Followers ≤ 0.002 entropy = 0.475 samples = 49 value = [44, 5] class = Not Popular 599->600 607 entropy = 0.0 samples = 14 value = [14, 0] class = Not Popular 599->607 601 entropy = 0.0 samples = 9 value = [9, 0] class = Not Popular 600->601 602 Artist_Followers ≤ 0.002 entropy = 0.544 samples = 40 value = [35, 5] class = Not Popular 600->602 603 entropy = 0.672 samples = 17 value = [14, 3] class = Not Popular 602->603 604 Artist_Followers ≤ 0.002 entropy = 0.426 samples = 23 value = [21, 2] class = Not Popular 602->604 605 entropy = 0.0 samples = 2 value = [2, 0] class = Not Popular 604->605 606 entropy = 0.454 samples = 21 value = [19, 2] class = Not Popular 604->606 609 entropy = 0.954 samples = 16 value = [6, 10] class = Popular 608->609 610 Artist_Popularity ≤ 0.19 entropy = 0.643 samples = 104 value = [87, 17] class = Not Popular 608->610 611 Artist_Followers ≤ 0.003 entropy = 0.511 samples = 44 value = [39, 5] class = Not Popular 610->611 616 Artist_Followers ≤ 0.003 entropy = 0.722 samples = 60 value = [48, 12] class = Not Popular 610->616 612 Artist_Followers ≤ 0.003 entropy = 0.353 samples = 30 value = [28, 2] class = Not Popular 611->612 615 entropy = 0.75 samples = 14 value = [11, 3] class = Not Popular 611->615 613 entropy = 0.469 samples = 10 value = [9, 1] class = Not Popular 612->613 614 entropy = 0.286 samples = 20 value = [19, 1] class = Not Popular 612->614 617 Artist_Followers ≤ 0.002 entropy = 0.753 samples = 37 value = [29, 8] class = Not Popular 616->617 620 entropy = 0.667 samples = 23 value = [19, 4] class = Not Popular 616->620 618 entropy = 0.764 samples = 18 value = [14, 4] class = Not Popular 617->618 619 entropy = 0.742 samples = 19 value = [15, 4] class = Not Popular 617->619 622 Artist_Followers ≤ 0.001 entropy = 0.936 samples = 142 value = [92, 50] class = Not Popular 621->622 651 Artist_Followers ≤ 0.002 entropy = 0.668 samples = 63 value = [52, 11] class = Not Popular 621->651 623 Artist_Followers ≤ 0.0 entropy = 0.843 samples = 107 value = [78, 29] class = Not Popular 622->623 644 Artist_Followers ≤ 0.002 entropy = 0.971 samples = 35 value = [14, 21] class = Popular 622->644 624 entropy = 0.0 samples = 5 value = [5, 0] class = Not Popular 623->624 625 Artist_Followers ≤ 0.0 entropy = 0.861 samples = 102 value = [73, 29] class = Not Popular 623->625 626 entropy = 0.0 samples = 3 value = [0, 3] class = Popular 625->626 627 Artist_Followers ≤ 0.001 entropy = 0.831 samples = 99 value = [73, 26] class = Not Popular 625->627 628 entropy = 0.684 samples = 22 value = [18, 4] class = Not Popular 627->628 629 Artist_Followers ≤ 0.001 entropy = 0.863 samples = 77 value = [55, 22] class = Not Popular 627->629 630 Artist_Followers ≤ 0.001 entropy = 0.946 samples = 22 value = [14, 8] class = Not Popular 629->630 633 Artist_Followers ≤ 0.001 entropy = 0.818 samples = 55 value = [41, 14] class = Not Popular 629->633 631 entropy = 0.863 samples = 14 value = [10, 4] class = Not Popular 630->631 632 entropy = 1.0 samples = 8 value = [4, 4] class = Not Popular 630->632 634 entropy = 0.0 samples = 5 value = [5, 0] class = Not Popular 633->634 635 Artist_Followers ≤ 0.001 entropy = 0.855 samples = 50 value = [36, 14] class = Not Popular 633->635 636 Artist_Followers ≤ 0.001 entropy = 0.755 samples = 23 value = [18, 5] class = Not Popular 635->636 639 Artist_Followers ≤ 0.001 entropy = 0.918 samples = 27 value = [18, 9] class = Not Popular 635->639 637 entropy = 0.918 samples = 12 value = [8, 4] class = Not Popular 636->637 638 entropy = 0.439 samples = 11 value = [10, 1] class = Not Popular 636->638 640 entropy = 0.98 samples = 12 value = [7, 5] class = Not Popular 639->640 641 Artist_Followers ≤ 0.001 entropy = 0.837 samples = 15 value = [11, 4] class = Not Popular 639->641 642 entropy = 0.811 samples = 4 value = [3, 1] class = Not Popular 641->642 643 entropy = 0.845 samples = 11 value = [8, 3] class = Not Popular 641->643 645 Artist_Followers ≤ 0.001 entropy = 0.996 samples = 13 value = [6, 7] class = Popular 644->645 648 Artist_Followers ≤ 0.002 entropy = 0.946 samples = 22 value = [8, 14] class = Popular 644->648 646 entropy = 0.946 samples = 11 value = [4, 7] class = Popular 645->646 647 entropy = 0.0 samples = 2 value = [2, 0] class = Not Popular 645->647 649 entropy = 0.89 samples = 13 value = [4, 9] class = Popular 648->649 650 entropy = 0.991 samples = 9 value = [4, 5] class = Popular 648->650 652 entropy = 0.0 samples = 4 value = [4, 0] class = Not Popular 651->652 653 Artist_Followers ≤ 0.002 entropy = 0.694 samples = 59 value = [48, 11] class = Not Popular 651->653 654 entropy = 0.75 samples = 42 value = [33, 9] class = Not Popular 653->654 655 entropy = 0.523 samples = 17 value = [15, 2] class = Not Popular 653->655 657 Artist_Followers ≤ 0.001 entropy = 0.818 samples = 59 value = [44, 15] class = Not Popular 656->657 670 Artist_Followers ≤ 0.002 entropy = 0.505 samples = 206 value = [183, 23] class = Not Popular 656->670 658 Artist_Followers ≤ 0.001 entropy = 0.789 samples = 55 value = [42, 13] class = Not Popular 657->658 669 entropy = 1.0 samples = 4 value = [2, 2] class = Not Popular 657->669 659 Artist_Followers ≤ 0.0 entropy = 0.918 samples = 21 value = [14, 7] class = Not Popular 658->659 664 Artist_Followers ≤ 0.001 entropy = 0.672 samples = 34 value = [28, 6] class = Not Popular 658->664 660 Artist_Followers ≤ 0.0 entropy = 0.619 samples = 13 value = [11, 2] class = Not Popular 659->660 663 entropy = 0.954 samples = 8 value = [3, 5] class = Popular 659->663 661 entropy = 0.0 samples = 1 value = [1, 0] class = Not Popular 660->661 662 entropy = 0.65 samples = 12 value = [10, 2] class = Not Popular 660->662 665 entropy = 0.691 samples = 27 value = [22, 5] class = Not Popular 664->665 666 Artist_Followers ≤ 0.001 entropy = 0.592 samples = 7 value = [6, 1] class = Not Popular 664->666 667 entropy = 0.0 samples = 1 value = [1, 0] class = Not Popular 666->667 668 entropy = 0.65 samples = 6 value = [5, 1] class = Not Popular 666->668 671 Artist_Followers ≤ 0.002 entropy = 0.452 samples = 169 value = [153, 16] class = Not Popular 670->671 688 Artist_Followers ≤ 0.003 entropy = 0.7 samples = 37 value = [30, 7] class = Not Popular 670->688 672 Artist_Followers ≤ 0.001 entropy = 0.483 samples = 153 value = [137, 16] class = Not Popular 671->672 687 entropy = 0.0 samples = 16 value = [16, 0] class = Not Popular 671->687 673 Artist_Followers ≤ 0.001 entropy = 0.406 samples = 37 value = [34, 3] class = Not Popular 672->673 678 Artist_Followers ≤ 0.002 entropy = 0.506 samples = 116 value = [103, 13] class = Not Popular 672->678 674 Artist_Followers ≤ 0.001 entropy = 0.503 samples = 27 value = [24, 3] class = Not Popular 673->674 677 entropy = 0.0 samples = 10 value = [10, 0] class = Not Popular 673->677 675 entropy = 0.469 samples = 10 value = [9, 1] class = Not Popular 674->675 676 entropy = 0.523 samples = 17 value = [15, 2] class = Not Popular 674->676 679 Artist_Followers ≤ 0.001 entropy = 0.529 samples = 75 value = [66, 9] class = Not Popular 678->679 686 entropy = 0.461 samples = 41 value = [37, 4] class = Not Popular 678->686 680 entropy = 0.469 samples = 20 value = [18, 2] class = Not Popular 679->680 681 Artist_Followers ≤ 0.002 entropy = 0.55 samples = 55 value = [48, 7] class = Not Popular 679->681 682 Artist_Followers ≤ 0.001 entropy = 0.571 samples = 37 value = [32, 5] class = Not Popular 681->682 685 entropy = 0.503 samples = 18 value = [16, 2] class = Not Popular 681->685 683 entropy = 0.579 samples = 29 value = [25, 4] class = Not Popular 682->683 684 entropy = 0.544 samples = 8 value = [7, 1] class = Not Popular 682->684 689 entropy = 0.918 samples = 12 value = [8, 4] class = Not Popular 688->689 690 entropy = 0.529 samples = 25 value = [22, 3] class = Not Popular 688->690 692 Artist_Popularity ≤ 0.278 entropy = 0.965 samples = 202 value = [123, 79] class = Not Popular 691->692 731 Artist_Followers ≤ 0.003 entropy = 0.83 samples = 1057 value = [780, 277] class = Not Popular 691->731 693 Artist_Followers ≤ 0.001 entropy = 0.934 samples = 120 value = [78, 42] class = Not Popular 692->693 714 Artist_Followers ≤ 0.001 entropy = 0.993 samples = 82 value = [45, 37] class = Not Popular 692->714 694 Artist_Followers ≤ 0.0 entropy = 0.905 samples = 103 value = [70, 33] class = Not Popular 693->694 713 entropy = 0.998 samples = 17 value = [8, 9] class = Popular 693->713 695 Artist_Followers ≤ 0.0 entropy = 1.0 samples = 16 value = [8, 8] class = Not Popular 694->695 698 Artist_Popularity ≤ 0.262 entropy = 0.865 samples = 87 value = [62, 25] class = Not Popular 694->698 696 entropy = 0.811 samples = 4 value = [3, 1] class = Not Popular 695->696 697 entropy = 0.98 samples = 12 value = [5, 7] class = Popular 695->697 699 Artist_Followers ≤ 0.001 entropy = 0.544 samples = 16 value = [14, 2] class = Not Popular 698->699 702 Artist_Followers ≤ 0.001 entropy = 0.909 samples = 71 value = [48, 23] class = Not Popular 698->702 700 entropy = 0.722 samples = 10 value = [8, 2] class = Not Popular 699->700 701 entropy = 0.0 samples = 6 value = [6, 0] class = Not Popular 699->701 703 Artist_Followers ≤ 0.001 entropy = 0.722 samples = 20 value = [16, 4] class = Not Popular 702->703 708 Artist_Followers ≤ 0.001 entropy = 0.953 samples = 51 value = [32, 19] class = Not Popular 702->708 704 Artist_Followers ≤ 0.0 entropy = 0.787 samples = 17 value = [13, 4] class = Not Popular 703->704 707 entropy = 0.0 samples = 3 value = [3, 0] class = Not Popular 703->707 705 entropy = 0.0 samples = 2 value = [2, 0] class = Not Popular 704->705 706 entropy = 0.837 samples = 15 value = [11, 4] class = Not Popular 704->706 709 entropy = 0.98 samples = 24 value = [14, 10] class = Not Popular 708->709 710 Artist_Followers ≤ 0.001 entropy = 0.918 samples = 27 value = [18, 9] class = Not Popular 708->710 711 entropy = 0.934 samples = 20 value = [13, 7] class = Not Popular 710->711 712 entropy = 0.863 samples = 7 value = [5, 2] class = Not Popular 710->712 715 Artist_Popularity ≤ 0.31 entropy = 0.982 samples = 76 value = [44, 32] class = Not Popular 714->715 730 entropy = 0.65 samples = 6 value = [1, 5] class = Popular 714->730 716 Artist_Followers ≤ 0.0 entropy = 0.988 samples = 71 value = [40, 31] class = Not Popular 715->716 729 entropy = 0.722 samples = 5 value = [4, 1] class = Not Popular 715->729 717 Artist_Followers ≤ 0.0 entropy = 0.985 samples = 7 value = [3, 4] class = Popular 716->717 720 Artist_Followers ≤ 0.0 entropy = 0.982 samples = 64 value = [37, 27] class = Not Popular 716->720 718 entropy = 1.0 samples = 6 value = [3, 3] class = Not Popular 717->718 719 entropy = 0.0 samples = 1 value = [0, 1] class = Popular 717->719 721 entropy = 0.811 samples = 12 value = [9, 3] class = Not Popular 720->721 722 Artist_Followers ≤ 0.0 entropy = 0.996 samples = 52 value = [28, 24] class = Not Popular 720->722 723 entropy = 0.65 samples = 6 value = [1, 5] class = Popular 722->723 724 Artist_Followers ≤ 0.001 entropy = 0.978 samples = 46 value = [27, 19] class = Not Popular 722->724 725 entropy = 0.918 samples = 18 value = [12, 6] class = Not Popular 724->725 726 Artist_Popularity ≤ 0.294 entropy = 0.996 samples = 28 value = [15, 13] class = Not Popular 724->726 727 entropy = 0.997 samples = 15 value = [8, 7] class = Not Popular 726->727 728 entropy = 0.996 samples = 13 value = [7, 6] class = Not Popular 726->728 732 Artist_Followers ≤ 0.002 entropy = 0.812 samples = 958 value = [718, 240] class = Not Popular 731->732 825 Artist_Popularity ≤ 0.294 entropy = 0.953 samples = 99 value = [62, 37] class = Not Popular 731->825 733 Artist_Followers ≤ 0.002 entropy = 0.848 samples = 791 value = [574, 217] class = Not Popular 732->733 812 Artist_Popularity ≤ 0.302 entropy = 0.578 samples = 167 value = [144, 23] class = Not Popular 732->812 734 Artist_Popularity ≤ 0.262 entropy = 0.791 samples = 514 value = [392, 122] class = Not Popular 733->734 787 Artist_Followers ≤ 0.002 entropy = 0.928 samples = 277 value = [182, 95] class = Not Popular 733->787 735 Artist_Followers ≤ 0.001 entropy = 0.469 samples = 40 value = [36, 4] class = Not Popular 734->735 740 Artist_Followers ≤ 0.002 entropy = 0.81 samples = 474 value = [356, 118] class = Not Popular 734->740 736 entropy = 0.544 samples = 16 value = [14, 2] class = Not Popular 735->736 737 Artist_Followers ≤ 0.001 entropy = 0.414 samples = 24 value = [22, 2] class = Not Popular 735->737 738 entropy = 0.414 samples = 12 value = [11, 1] class = Not Popular 737->738 739 entropy = 0.414 samples = 12 value = [11, 1] class = Not Popular 737->739 741 Artist_Popularity ≤ 0.294 entropy = 0.814 samples = 469 value = [351, 118] class = Not Popular 740->741 786 entropy = 0.0 samples = 5 value = [5, 0] class = Not Popular 740->786 742 Artist_Followers ≤ 0.002 entropy = 0.874 samples = 177 value = [125, 52] class = Not Popular 741->742 763 Artist_Followers ≤ 0.001 entropy = 0.771 samples = 292 value = [226, 66] class = Not Popular 741->763 743 Artist_Followers ≤ 0.001 entropy = 0.848 samples = 171 value = [124, 47] class = Not Popular 742->743 762 entropy = 0.65 samples = 6 value = [1, 5] class = Popular 742->762 744 Artist_Followers ≤ 0.001 entropy = 0.774 samples = 79 value = [61, 18] class = Not Popular 743->744 753 Artist_Followers ≤ 0.001 entropy = 0.899 samples = 92 value = [63, 29] class = Not Popular 743->753 745 Artist_Followers ≤ 0.001 entropy = 0.811 samples = 64 value = [48, 16] class = Not Popular 744->745 752 entropy = 0.567 samples = 15 value = [13, 2] class = Not Popular 744->752 746 entropy = 0.863 samples = 14 value = [10, 4] class = Not Popular 745->746 747 Artist_Popularity ≤ 0.278 entropy = 0.795 samples = 50 value = [38, 12] class = Not Popular 745->747 748 entropy = 0.722 samples = 20 value = [16, 4] class = Not Popular 747->748 749 Artist_Followers ≤ 0.001 entropy = 0.837 samples = 30 value = [22, 8] class = Not Popular 747->749 750 entropy = 0.918 samples = 9 value = [6, 3] class = Not Popular 749->750 751 entropy = 0.792 samples = 21 value = [16, 5] class = Not Popular 749->751 754 entropy = 0.998 samples = 17 value = [9, 8] class = Not Popular 753->754 755 Artist_Followers ≤ 0.002 entropy = 0.855 samples = 75 value = [54, 21] class = Not Popular 753->755 756 Artist_Followers ≤ 0.001 entropy = 0.869 samples = 69 value = [49, 20] class = Not Popular 755->756 761 entropy = 0.65 samples = 6 value = [5, 1] class = Not Popular 755->761 757 Artist_Followers ≤ 0.001 entropy = 0.89 samples = 39 value = [27, 12] class = Not Popular 756->757 760 entropy = 0.837 samples = 30 value = [22, 8] class = Not Popular 756->760 758 entropy = 0.89 samples = 26 value = [18, 8] class = Not Popular 757->758 759 entropy = 0.89 samples = 13 value = [9, 4] class = Not Popular 757->759 764 Artist_Followers ≤ 0.001 entropy = 0.809 samples = 217 value = [163, 54] class = Not Popular 763->764 781 Artist_Popularity ≤ 0.31 entropy = 0.634 samples = 75 value = [63, 12] class = Not Popular 763->781 765 Artist_Followers ≤ 0.001 entropy = 0.718 samples = 116 value = [93, 23] class = Not Popular 764->765 772 Artist_Followers ≤ 0.001 entropy = 0.89 samples = 101 value = [70, 31] class = Not Popular 764->772 766 Artist_Followers ≤ 0.001 entropy = 0.668 samples = 63 value = [52, 11] class = Not Popular 765->766 769 Artist_Followers ≤ 0.001 entropy = 0.772 samples = 53 value = [41, 12] class = Not Popular 765->769 767 entropy = 0.667 samples = 46 value = [38, 8] class = Not Popular 766->767 768 entropy = 0.672 samples = 17 value = [14, 3] class = Not Popular 766->768 770 entropy = 0.826 samples = 27 value = [20, 7] class = Not Popular 769->770 771 entropy = 0.706 samples = 26 value = [21, 5] class = Not Popular 769->771 773 entropy = 0.0 samples = 9 value = [0, 9] class = Popular 772->773 774 Artist_Popularity ≤ 0.31 entropy = 0.794 samples = 92 value = [70, 22] class = Not Popular 772->774 775 entropy = 0.469 samples = 10 value = [9, 1] class = Not Popular 774->775 776 Artist_Followers ≤ 0.001 entropy = 0.821 samples = 82 value = [61, 21] class = Not Popular 774->776 777 Artist_Followers ≤ 0.001 entropy = 0.768 samples = 49 value = [38, 11] class = Not Popular 776->777 780 entropy = 0.885 samples = 33 value = [23, 10] class = Not Popular 776->780 778 entropy = 0.742 samples = 19 value = [15, 4] class = Not Popular 777->778 779 entropy = 0.784 samples = 30 value = [23, 7] class = Not Popular 777->779 782 Artist_Followers ≤ 0.002 entropy = 0.672 samples = 68 value = [56, 12] class = Not Popular 781->782 785 entropy = 0.0 samples = 7 value = [7, 0] class = Not Popular 781->785 783 entropy = 0.555 samples = 31 value = [27, 4] class = Not Popular 782->783 784 entropy = 0.753 samples = 37 value = [29, 8] class = Not Popular 782->784 788 Artist_Popularity ≤ 0.278 entropy = 0.999 samples = 79 value = [41, 38] class = Not Popular 787->788 797 Artist_Followers ≤ 0.002 entropy = 0.866 samples = 198 value = [141, 57] class = Not Popular 787->797 789 Artist_Popularity ≤ 0.262 entropy = 0.993 samples = 73 value = [40, 33] class = Not Popular 788->789 796 entropy = 0.65 samples = 6 value = [1, 5] class = Popular 788->796 790 Artist_Followers ≤ 0.002 entropy = 1.0 samples = 41 value = [21, 20] class = Not Popular 789->790 793 Artist_Followers ≤ 0.002 entropy = 0.974 samples = 32 value = [19, 13] class = Not Popular 789->793 791 entropy = 0.988 samples = 23 value = [13, 10] class = Not Popular 790->791 792 entropy = 0.991 samples = 18 value = [8, 10] class = Popular 790->792 794 entropy = 0.954 samples = 16 value = [10, 6] class = Not Popular 793->794 795 entropy = 0.989 samples = 16 value = [9, 7] class = Not Popular 793->795 798 Artist_Followers ≤ 0.002 entropy = 0.722 samples = 95 value = [76, 19] class = Not Popular 797->798 805 Artist_Followers ≤ 0.002 entropy = 0.95 samples = 103 value = [65, 38] class = Not Popular 797->805 799 Artist_Followers ≤ 0.002 entropy = 0.84 samples = 52 value = [38, 14] class = Not Popular 798->799 802 Artist_Followers ≤ 0.002 entropy = 0.519 samples = 43 value = [38, 5] class = Not Popular 798->802 800 entropy = 0.734 samples = 34 value = [27, 7] class = Not Popular 799->800 801 entropy = 0.964 samples = 18 value = [11, 7] class = Not Popular 799->801 803 entropy = 0.592 samples = 14 value = [12, 2] class = Not Popular 802->803 804 entropy = 0.48 samples = 29 value = [26, 3] class = Not Popular 802->804 806 entropy = 1.0 samples = 20 value = [10, 10] class = Not Popular 805->806 807 Artist_Followers ≤ 0.002 entropy = 0.922 samples = 83 value = [55, 28] class = Not Popular 805->807 808 entropy = 0.828 samples = 23 value = [17, 6] class = Not Popular 807->808 809 Artist_Followers ≤ 0.002 entropy = 0.948 samples = 60 value = [38, 22] class = Not Popular 807->809 810 entropy = 0.991 samples = 18 value = [10, 8] class = Not Popular 809->810 811 entropy = 0.918 samples = 42 value = [28, 14] class = Not Popular 809->811 813 Artist_Followers ≤ 0.002 entropy = 0.339 samples = 111 value = [104, 7] class = Not Popular 812->813 822 Artist_Followers ≤ 0.003 entropy = 0.863 samples = 56 value = [40, 16] class = Not Popular 812->822 814 entropy = 0.503 samples = 9 value = [8, 1] class = Not Popular 813->814 815 Artist_Followers ≤ 0.002 entropy = 0.323 samples = 102 value = [96, 6] class = Not Popular 813->815 816 entropy = 0.371 samples = 28 value = [26, 2] class = Not Popular 815->816 817 Artist_Followers ≤ 0.003 entropy = 0.303 samples = 74 value = [70, 4] class = Not Popular 815->817 818 Artist_Popularity ≤ 0.27 entropy = 0.318 samples = 52 value = [49, 3] class = Not Popular 817->818 821 entropy = 0.267 samples = 22 value = [21, 1] class = Not Popular 817->821 819 entropy = 0.297 samples = 19 value = [18, 1] class = Not Popular 818->819 820 entropy = 0.33 samples = 33 value = [31, 2] class = Not Popular 818->820 823 entropy = 0.773 samples = 22 value = [17, 5] class = Not Popular 822->823 824 entropy = 0.908 samples = 34 value = [23, 11] class = Not Popular 822->824 826 Artist_Popularity ≤ 0.278 entropy = 1.0 samples = 46 value = [23, 23] class = Not Popular 825->826 829 Artist_Followers ≤ 0.003 entropy = 0.833 samples = 53 value = [39, 14] class = Not Popular 825->829 827 entropy = 0.985 samples = 14 value = [8, 6] class = Not Popular 826->827 828 entropy = 0.997 samples = 32 value = [15, 17] class = Popular 826->828 830 entropy = 0.592 samples = 28 value = [24, 4] class = Not Popular 829->830 831 entropy = 0.971 samples = 25 value = [15, 10] class = Not Popular 829->831 833 Artist_Followers ≤ 0.008 entropy = 0.638 samples = 3176 value = [2663, 513] class = Not Popular 832->833 1038 Artist_Popularity ≤ 0.27 entropy = 1.0 samples = 39 value = [20, 19] class = Not Popular 832->1038 834 Artist_Followers ≤ 0.005 entropy = 0.608 samples = 2313 value = [1968, 345] class = Not Popular 833->834 993 Artist_Followers ≤ 0.011 entropy = 0.711 samples = 863 value = [695, 168] class = Not Popular 833->993 835 Artist_Popularity ≤ 0.294 entropy = 0.656 samples = 1046 value = [869, 177] class = Not Popular 834->835 918 Artist_Popularity ≤ 0.294 entropy = 0.565 samples = 1267 value = [1099, 168] class = Not Popular 834->918 836 Artist_Followers ≤ 0.003 entropy = 0.684 samples = 809 value = [662, 147] class = Not Popular 835->836 905 Artist_Followers ≤ 0.004 entropy = 0.548 samples = 237 value = [207, 30] class = Not Popular 835->905 837 Artist_Followers ≤ 0.003 entropy = 0.826 samples = 77 value = [57, 20] class = Not Popular 836->837 846 Artist_Followers ≤ 0.003 entropy = 0.666 samples = 732 value = [605, 127] class = Not Popular 836->846 838 entropy = 0.391 samples = 13 value = [12, 1] class = Not Popular 837->838 839 Artist_Popularity ≤ 0.262 entropy = 0.877 samples = 64 value = [45, 19] class = Not Popular 837->839 840 Artist_Followers ≤ 0.003 entropy = 0.954 samples = 16 value = [10, 6] class = Not Popular 839->840 843 Artist_Popularity ≤ 0.278 entropy = 0.843 samples = 48 value = [35, 13] class = Not Popular 839->843 841 entropy = 0.994 samples = 11 value = [6, 5] class = Not Popular 840->841 842 entropy = 0.722 samples = 5 value = [4, 1] class = Not Popular 840->842 844 entropy = 0.787 samples = 17 value = [13, 4] class = Not Popular 843->844 845 entropy = 0.869 samples = 31 value = [22, 9] class = Not Popular 843->845 847 entropy = 0.25 samples = 24 value = [23, 1] class = Not Popular 846->847 848 Artist_Popularity ≤ 0.278 entropy = 0.676 samples = 708 value = [582, 126] class = Not Popular 846->848 849 Artist_Popularity ≤ 0.246 entropy = 0.694 samples = 617 value = [502, 115] class = Not Popular 848->849 902 Artist_Followers ≤ 0.005 entropy = 0.532 samples = 91 value = [80, 11] class = Not Popular 848->902 850 Artist_Followers ≤ 0.004 entropy = 0.724 samples = 333 value = [266, 67] class = Not Popular 849->850 883 Artist_Followers ≤ 0.004 entropy = 0.655 samples = 284 value = [236, 48] class = Not Popular 849->883 851 Artist_Followers ≤ 0.004 entropy = 0.625 samples = 128 value = [108, 20] class = Not Popular 850->851 866 Artist_Followers ≤ 0.004 entropy = 0.777 samples = 205 value = [158, 47] class = Not Popular 850->866 852 Artist_Followers ≤ 0.004 entropy = 0.682 samples = 83 value = [68, 15] class = Not Popular 851->852 863 Artist_Followers ≤ 0.004 entropy = 0.503 samples = 45 value = [40, 5] class = Not Popular 851->863 853 Artist_Followers ≤ 0.003 entropy = 0.592 samples = 70 value = [60, 10] class = Not Popular 852->853 860 Artist_Followers ≤ 0.004 entropy = 0.961 samples = 13 value = [8, 5] class = Not Popular 852->860 854 Artist_Followers ≤ 0.003 entropy = 0.677 samples = 28 value = [23, 5] class = Not Popular 853->854 857 Artist_Followers ≤ 0.004 entropy = 0.527 samples = 42 value = [37, 5] class = Not Popular 853->857 855 entropy = 0.439 samples = 11 value = [10, 1] class = Not Popular 854->855 856 entropy = 0.787 samples = 17 value = [13, 4] class = Not Popular 854->856 858 entropy = 0.0 samples = 13 value = [13, 0] class = Not Popular 857->858 859 entropy = 0.663 samples = 29 value = [24, 5] class = Not Popular 857->859 861 entropy = 0.946 samples = 11 value = [7, 4] class = Not Popular 860->861 862 entropy = 1.0 samples = 2 value = [1, 1] class = Not Popular 860->862 864 entropy = 0.439 samples = 22 value = [20, 2] class = Not Popular 863->864 865 entropy = 0.559 samples = 23 value = [20, 3] class = Not Popular 863->865 867 Artist_Followers ≤ 0.004 entropy = 0.991 samples = 18 value = [8, 10] class = Popular 866->867 870 Artist_Popularity ≤ 0.198 entropy = 0.718 samples = 187 value = [150, 37] class = Not Popular 866->870 868 entropy = 1.0 samples = 8 value = [4, 4] class = Not Popular 867->868 869 entropy = 0.971 samples = 10 value = [4, 6] class = Popular 867->869 871 entropy = 0.406 samples = 37 value = [34, 3] class = Not Popular 870->871 872 Artist_Popularity ≤ 0.214 entropy = 0.772 samples = 150 value = [116, 34] class = Not Popular 870->872 873 Artist_Followers ≤ 0.005 entropy = 0.822 samples = 35 value = [26, 9] class = Not Popular 872->873 876 Artist_Followers ≤ 0.005 entropy = 0.755 samples = 115 value = [90, 25] class = Not Popular 872->876 874 entropy = 0.722 samples = 5 value = [4, 1] class = Not Popular 873->874 875 entropy = 0.837 samples = 30 value = [22, 8] class = Not Popular 873->875 877 Artist_Followers ≤ 0.005 entropy = 0.736 samples = 82 value = [65, 17] class = Not Popular 876->877 882 entropy = 0.799 samples = 33 value = [25, 8] class = Not Popular 876->882 878 Artist_Popularity ≤ 0.23 entropy = 0.772 samples = 53 value = [41, 12] class = Not Popular 877->878 881 entropy = 0.663 samples = 29 value = [24, 5] class = Not Popular 877->881 879 entropy = 0.773 samples = 22 value = [17, 5] class = Not Popular 878->879 880 entropy = 0.771 samples = 31 value = [24, 7] class = Not Popular 878->880 884 Artist_Popularity ≤ 0.262 entropy = 0.705 samples = 235 value = [190, 45] class = Not Popular 883->884 897 Artist_Followers ≤ 0.005 entropy = 0.332 samples = 49 value = [46, 3] class = Not Popular 883->897 885 Artist_Followers ≤ 0.004 entropy = 0.603 samples = 95 value = [81, 14] class = Not Popular 884->885 890 Artist_Followers ≤ 0.003 entropy = 0.763 samples = 140 value = [109, 31] class = Not Popular 884->890 886 entropy = 0.75 samples = 14 value = [11, 3] class = Not Popular 885->886 887 Artist_Followers ≤ 0.004 entropy = 0.573 samples = 81 value = [70, 11] class = Not Popular 885->887 888 entropy = 0.571 samples = 37 value = [32, 5] class = Not Popular 887->888 889 entropy = 0.575 samples = 44 value = [38, 6] class = Not Popular 887->889 891 entropy = 0.667 samples = 46 value = [38, 8] class = Not Popular 890->891 892 Artist_Followers ≤ 0.004 entropy = 0.803 samples = 94 value = [71, 23] class = Not Popular 890->892 893 Artist_Followers ≤ 0.004 entropy = 0.787 samples = 68 value = [52, 16] class = Not Popular 892->893 896 entropy = 0.84 samples = 26 value = [19, 7] class = Not Popular 892->896 894 entropy = 0.826 samples = 27 value = [20, 7] class = Not Popular 893->894 895 entropy = 0.759 samples = 41 value = [32, 9] class = Not Popular 893->895 898 entropy = 0.0 samples = 17 value = [17, 0] class = Not Popular 897->898 899 Artist_Popularity ≤ 0.262 entropy = 0.449 samples = 32 value = [29, 3] class = Not Popular 897->899 900 entropy = 0.485 samples = 19 value = [17, 2] class = Not Popular 899->900 901 entropy = 0.391 samples = 13 value = [12, 1] class = Not Popular 899->901 903 entropy = 0.391 samples = 52 value = [48, 4] class = Not Popular 902->903 904 entropy = 0.679 samples = 39 value = [32, 7] class = Not Popular 902->904 906 Artist_Followers ≤ 0.003 entropy = 0.474 samples = 207 value = [186, 21] class = Not Popular 905->906 917 entropy = 0.881 samples = 30 value = [21, 9] class = Not Popular 905->917 907 Artist_Followers ≤ 0.003 entropy = 0.741 samples = 62 value = [49, 13] class = Not Popular 906->907 912 Artist_Popularity ≤ 0.31 entropy = 0.308 samples = 145 value = [137, 8] class = Not Popular 906->912 908 Artist_Followers ≤ 0.003 entropy = 0.605 samples = 54 value = [46, 8] class = Not Popular 907->908 911 entropy = 0.954 samples = 8 value = [3, 5] class = Popular 907->911 909 entropy = 0.431 samples = 34 value = [31, 3] class = Not Popular 908->909 910 entropy = 0.811 samples = 20 value = [15, 5] class = Not Popular 908->910 913 Artist_Followers ≤ 0.003 entropy = 0.303 samples = 111 value = [105, 6] class = Not Popular 912->913 916 entropy = 0.323 samples = 34 value = [32, 2] class = Not Popular 912->916 914 entropy = 0.303 samples = 74 value = [70, 4] class = Not Popular 913->914 915 entropy = 0.303 samples = 37 value = [35, 2] class = Not Popular 913->915 919 Artist_Followers ≤ 0.006 entropy = 0.542 samples = 1101 value = [964, 137] class = Not Popular 918->919 982 Artist_Followers ≤ 0.006 entropy = 0.695 samples = 166 value = [135, 31] class = Not Popular 918->982 920 Artist_Followers ≤ 0.006 entropy = 0.581 samples = 663 value = [571, 92] class = Not Popular 919->920 961 Artist_Followers ≤ 0.007 entropy = 0.478 samples = 438 value = [393, 45] class = Not Popular 919->961 921 Artist_Followers ≤ 0.005 entropy = 0.547 samples = 594 value = [519, 75] class = Not Popular 920->921 958 Artist_Followers ≤ 0.006 entropy = 0.805 samples = 69 value = [52, 17] class = Not Popular 920->958 922 Artist_Popularity ≤ 0.19 entropy = 0.353 samples = 60 value = [56, 4] class = Not Popular 921->922 925 Artist_Followers ≤ 0.005 entropy = 0.565 samples = 534 value = [463, 71] class = Not Popular 921->925 923 entropy = 0.371 samples = 28 value = [26, 2] class = Not Popular 922->923 924 entropy = 0.337 samples = 32 value = [30, 2] class = Not Popular 922->924 926 Artist_Followers ≤ 0.005 entropy = 0.67 samples = 57 value = [47, 10] class = Not Popular 925->926 929 Artist_Followers ≤ 0.005 entropy = 0.552 samples = 477 value = [416, 61] class = Not Popular 925->929 927 entropy = 0.689 samples = 38 value = [31, 7] class = Not Popular 926->927 928 entropy = 0.629 samples = 19 value = [16, 3] class = Not Popular 926->928 930 Artist_Followers ≤ 0.005 entropy = 0.424 samples = 81 value = [74, 7] class = Not Popular 929->930 935 Artist_Followers ≤ 0.006 entropy = 0.575 samples = 396 value = [342, 54] class = Not Popular 929->935 931 Artist_Popularity ≤ 0.222 entropy = 0.485 samples = 57 value = [51, 6] class = Not Popular 930->931 934 entropy = 0.25 samples = 24 value = [23, 1] class = Not Popular 930->934 932 entropy = 0.503 samples = 18 value = [16, 2] class = Not Popular 931->932 933 entropy = 0.477 samples = 39 value = [35, 4] class = Not Popular 931->933 936 Artist_Followers ≤ 0.006 entropy = 0.606 samples = 249 value = [212, 37] class = Not Popular 935->936 951 Artist_Followers ≤ 0.006 entropy = 0.517 samples = 147 value = [130, 17] class = Not Popular 935->951 937 Artist_Followers ≤ 0.005 entropy = 0.533 samples = 173 value = [152, 21] class = Not Popular 936->937 946 Artist_Followers ≤ 0.006 entropy = 0.742 samples = 76 value = [60, 16] class = Not Popular 936->946 938 Artist_Followers ≤ 0.005 entropy = 0.546 samples = 135 value = [118, 17] class = Not Popular 937->938 945 entropy = 0.485 samples = 38 value = [34, 4] class = Not Popular 937->945 939 entropy = 0.511 samples = 44 value = [39, 5] class = Not Popular 938->939 940 Artist_Popularity ≤ 0.254 entropy = 0.563 samples = 91 value = [79, 12] class = Not Popular 938->940 941 entropy = 0.575 samples = 44 value = [38, 6] class = Not Popular 940->941 942 Artist_Popularity ≤ 0.278 entropy = 0.551 samples = 47 value = [41, 6] class = Not Popular 940->942 943 entropy = 0.552 samples = 39 value = [34, 5] class = Not Popular 942->943 944 entropy = 0.544 samples = 8 value = [7, 1] class = Not Popular 942->944 947 entropy = 0.811 samples = 40 value = [30, 10] class = Not Popular 946->947 948 Artist_Popularity ≤ 0.238 entropy = 0.65 samples = 36 value = [30, 6] class = Not Popular 946->948 949 entropy = 0.65 samples = 18 value = [15, 3] class = Not Popular 948->949 950 entropy = 0.65 samples = 18 value = [15, 3] class = Not Popular 948->950 952 entropy = 0.0 samples = 26 value = [26, 0] class = Not Popular 951->952 953 Artist_Followers ≤ 0.006 entropy = 0.586 samples = 121 value = [104, 17] class = Not Popular 951->953 954 entropy = 0.494 samples = 37 value = [33, 4] class = Not Popular 953->954 955 Artist_Popularity ≤ 0.222 entropy = 0.622 samples = 84 value = [71, 13] class = Not Popular 953->955 956 entropy = 0.667 samples = 23 value = [19, 4] class = Not Popular 955->956 957 entropy = 0.604 samples = 61 value = [52, 9] class = Not Popular 955->957 959 entropy = 0.928 samples = 35 value = [23, 12] class = Not Popular 958->959 960 entropy = 0.602 samples = 34 value = [29, 5] class = Not Popular 958->960 962 Artist_Popularity ≤ 0.278 entropy = 0.412 samples = 254 value = [233, 21] class = Not Popular 961->962 975 Artist_Followers ≤ 0.007 entropy = 0.559 samples = 184 value = [160, 24] class = Not Popular 961->975 963 Artist_Popularity ≤ 0.238 entropy = 0.396 samples = 217 value = [200, 17] class = Not Popular 962->963 974 entropy = 0.494 samples = 37 value = [33, 4] class = Not Popular 962->974 964 Artist_Popularity ≤ 0.198 entropy = 0.451 samples = 106 value = [96, 10] class = Not Popular 963->964 969 Artist_Popularity ≤ 0.262 entropy = 0.339 samples = 111 value = [104, 7] class = Not Popular 963->969 965 entropy = 0.402 samples = 25 value = [23, 2] class = Not Popular 964->965 966 Artist_Followers ≤ 0.007 entropy = 0.465 samples = 81 value = [73, 8] class = Not Popular 964->966 967 entropy = 0.496 samples = 46 value = [41, 5] class = Not Popular 966->967 968 entropy = 0.422 samples = 35 value = [32, 3] class = Not Popular 966->968 970 entropy = 0.176 samples = 38 value = [37, 1] class = Not Popular 969->970 971 Artist_Followers ≤ 0.007 entropy = 0.41 samples = 73 value = [67, 6] class = Not Popular 969->971 972 entropy = 0.397 samples = 51 value = [47, 4] class = Not Popular 971->972 973 entropy = 0.439 samples = 22 value = [20, 2] class = Not Popular 971->973 976 Artist_Followers ≤ 0.007 entropy = 0.659 samples = 82 value = [68, 14] class = Not Popular 975->976 979 Artist_Popularity ≤ 0.278 entropy = 0.463 samples = 102 value = [92, 10] class = Not Popular 975->979 977 entropy = 0.679 samples = 39 value = [32, 7] class = Not Popular 976->977 978 entropy = 0.641 samples = 43 value = [36, 7] class = Not Popular 976->978 980 entropy = 0.376 samples = 55 value = [51, 4] class = Not Popular 979->980 981 entropy = 0.551 samples = 47 value = [41, 6] class = Not Popular 979->981 983 Artist_Followers ≤ 0.005 entropy = 0.58 samples = 65 value = [56, 9] class = Not Popular 982->983 986 Artist_Followers ≤ 0.007 entropy = 0.756 samples = 101 value = [79, 22] class = Not Popular 982->986 984 entropy = 0.658 samples = 47 value = [39, 8] class = Not Popular 983->984 985 entropy = 0.31 samples = 18 value = [17, 1] class = Not Popular 983->985 987 Artist_Followers ≤ 0.006 entropy = 0.819 samples = 51 value = [38, 13] class = Not Popular 986->987 990 Artist_Popularity ≤ 0.31 entropy = 0.68 samples = 50 value = [41, 9] class = Not Popular 986->990 988 entropy = 0.738 samples = 24 value = [19, 5] class = Not Popular 987->988 989 entropy = 0.877 samples = 27 value = [19, 8] class = Not Popular 987->989 991 entropy = 0.672 samples = 17 value = [14, 3] class = Not Popular 990->991 992 entropy = 0.684 samples = 33 value = [27, 6] class = Not Popular 990->992 994 Artist_Followers ≤ 0.011 entropy = 0.748 samples = 694 value = [546, 148] class = Not Popular 993->994 1031 Artist_Followers ≤ 0.014 entropy = 0.525 samples = 169 value = [149, 20] class = Not Popular 993->1031 995 Artist_Followers ≤ 0.008 entropy = 0.724 samples = 676 value = [540, 136] class = Not Popular 994->995 1030 entropy = 0.918 samples = 18 value = [6, 12] class = Popular 994->1030 996 Artist_Popularity ≤ 0.27 entropy = 0.863 samples = 63 value = [45, 18] class = Not Popular 995->996 999 Artist_Followers ≤ 0.008 entropy = 0.707 samples = 613 value = [495, 118] class = Not Popular 995->999 997 entropy = 0.797 samples = 29 value = [22, 7] class = Not Popular 996->997 998 entropy = 0.908 samples = 34 value = [23, 11] class = Not Popular 996->998 1000 entropy = 0.391 samples = 52 value = [48, 4] class = Not Popular 999->1000 1001 Artist_Followers ≤ 0.009 entropy = 0.728 samples = 561 value = [447, 114] class = Not Popular 999->1001 1002 Artist_Followers ≤ 0.008 entropy = 0.657 samples = 206 value = [171, 35] class = Not Popular 1001->1002 1011 Artist_Followers ≤ 0.01 entropy = 0.765 samples = 355 value = [276, 79] class = Not Popular 1001->1011 1003 Artist_Popularity ≤ 0.278 entropy = 0.829 samples = 65 value = [48, 17] class = Not Popular 1002->1003 1006 Artist_Popularity ≤ 0.294 entropy = 0.551 samples = 141 value = [123, 18] class = Not Popular 1002->1006 1004 entropy = 0.65 samples = 42 value = [35, 7] class = Not Popular 1003->1004 1005 entropy = 0.988 samples = 23 value = [13, 10] class = Not Popular 1003->1005 1007 entropy = 0.408 samples = 49 value = [45, 4] class = Not Popular 1006->1007 1008 Artist_Followers ≤ 0.008 entropy = 0.615 samples = 92 value = [78, 14] class = Not Popular 1006->1008 1009 entropy = 0.667 samples = 46 value = [38, 8] class = Not Popular 1008->1009 1010 entropy = 0.559 samples = 46 value = [40, 6] class = Not Popular 1008->1010 1012 Artist_Followers ≤ 0.009 entropy = 0.857 samples = 146 value = [105, 41] class = Not Popular 1011->1012 1021 Artist_Popularity ≤ 0.254 entropy = 0.684 samples = 209 value = [171, 38] class = Not Popular 1011->1021 1013 Artist_Popularity ≤ 0.254 entropy = 0.764 samples = 126 value = [98, 28] class = Not Popular 1012->1013 1020 entropy = 0.934 samples = 20 value = [7, 13] class = Popular 1012->1020 1014 entropy = 0.918 samples = 6 value = [2, 4] class = Popular 1013->1014 1015 Artist_Followers ≤ 0.009 entropy = 0.722 samples = 120 value = [96, 24] class = Not Popular 1013->1015 1016 Artist_Followers ≤ 0.009 entropy = 0.667 samples = 92 value = [76, 16] class = Not Popular 1015->1016 1019 entropy = 0.863 samples = 28 value = [20, 8] class = Not Popular 1015->1019 1017 entropy = 0.824 samples = 31 value = [23, 8] class = Not Popular 1016->1017 1018 entropy = 0.561 samples = 61 value = [53, 8] class = Not Popular 1016->1018 1022 entropy = 0.571 samples = 37 value = [32, 5] class = Not Popular 1021->1022 1023 Artist_Popularity ≤ 0.278 entropy = 0.705 samples = 172 value = [139, 33] class = Not Popular 1021->1023 1024 entropy = 0.954 samples = 8 value = [3, 5] class = Popular 1023->1024 1025 Artist_Followers ≤ 0.01 entropy = 0.659 samples = 164 value = [136, 28] class = Not Popular 1023->1025 1026 entropy = 0.777 samples = 48 value = [37, 11] class = Not Popular 1025->1026 1027 Artist_Followers ≤ 0.011 entropy = 0.601 samples = 116 value = [99, 17] class = Not Popular 1025->1027 1028 entropy = 0.584 samples = 50 value = [43, 7] class = Not Popular 1027->1028 1029 entropy = 0.614 samples = 66 value = [56, 10] class = Not Popular 1027->1029 1032 Artist_Followers ≤ 0.013 entropy = 0.353 samples = 90 value = [84, 6] class = Not Popular 1031->1032 1035 Artist_Followers ≤ 0.016 entropy = 0.674 samples = 79 value = [65, 14] class = Not Popular 1031->1035 1033 entropy = 0.602 samples = 34 value = [29, 5] class = Not Popular 1032->1033 1034 entropy = 0.129 samples = 56 value = [55, 1] class = Not Popular 1032->1034 1036 entropy = 0.722 samples = 25 value = [20, 5] class = Not Popular 1035->1036 1037 entropy = 0.65 samples = 54 value = [45, 9] class = Not Popular 1035->1037 1039 entropy = 0.982 samples = 19 value = [11, 8] class = Not Popular 1038->1039 1040 entropy = 0.993 samples = 20 value = [9, 11] class = Popular 1038->1040 1042 Artist_Popularity ≤ 0.437 entropy = 0.952 samples = 10928 value = [6865, 4063] class = Not Popular 1041->1042 1593 Artist_Popularity ≤ 0.492 entropy = 0.78 samples = 212 value = [49, 163] class = Popular 1041->1593 1043 Artist_Followers ≤ 0.019 entropy = 0.915 samples = 5048 value = [3382, 1666] class = Not Popular 1042->1043 1348 Artist_Followers ≤ 0.06 entropy = 0.975 samples = 5880 value = [3483, 2397] class = Not Popular 1042->1348 1044 Artist_Followers ≤ 0.002 entropy = 0.902 samples = 4411 value = [3009, 1402] class = Not Popular 1043->1044 1323 Artist_Followers ≤ 0.019 entropy = 0.979 samples = 637 value = [373, 264] class = Not Popular 1043->1323 1045 Artist_Followers ≤ 0.002 entropy = 0.986 samples = 395 value = [225, 170] class = Not Popular 1044->1045 1098 Artist_Followers ≤ 0.01 entropy = 0.889 samples = 4016 value = [2784, 1232] class = Not Popular 1044->1098 1046 Artist_Followers ≤ 0.001 entropy = 0.978 samples = 377 value = [221, 156] class = Not Popular 1045->1046 1097 entropy = 0.764 samples = 18 value = [4, 14] class = Popular 1045->1097 1047 Artist_Followers ≤ 0.001 entropy = 0.997 samples = 73 value = [34, 39] class = Popular 1046->1047 1062 Artist_Popularity ≤ 0.341 entropy = 0.961 samples = 304 value = [187, 117] class = Not Popular 1046->1062 1048 Artist_Popularity ≤ 0.381 entropy = 0.986 samples = 58 value = [33, 25] class = Not Popular 1047->1048 1061 entropy = 0.353 samples = 15 value = [1, 14] class = Popular 1047->1061 1049 Artist_Followers ≤ 0.0 entropy = 0.896 samples = 32 value = [22, 10] class = Not Popular 1048->1049 1056 Artist_Popularity ≤ 0.421 entropy = 0.983 samples = 26 value = [11, 15] class = Popular 1048->1056 1050 entropy = 0.918 samples = 6 value = [2, 4] class = Popular 1049->1050 1051 Artist_Followers ≤ 0.0 entropy = 0.779 samples = 26 value = [20, 6] class = Not Popular 1049->1051 1052 entropy = 0.918 samples = 6 value = [4, 2] class = Not Popular 1051->1052 1053 Artist_Followers ≤ 0.001 entropy = 0.722 samples = 20 value = [16, 4] class = Not Popular 1051->1053 1054 entropy = 0.75 samples = 14 value = [11, 3] class = Not Popular 1053->1054 1055 entropy = 0.65 samples = 6 value = [5, 1] class = Not Popular 1053->1055 1057 Artist_Followers ≤ 0.0 entropy = 0.99 samples = 25 value = [11, 14] class = Popular 1056->1057 1060 entropy = 0.0 samples = 1 value = [0, 1] class = Popular 1056->1060 1058 entropy = 0.994 samples = 11 value = [5, 6] class = Popular 1057->1058 1059 entropy = 0.985 samples = 14 value = [6, 8] class = Popular 1057->1059 1063 Artist_Followers ≤ 0.001 entropy = 0.835 samples = 79 value = [58, 21] class = Not Popular 1062->1063 1070 Artist_Popularity ≤ 0.389 entropy = 0.984 samples = 225 value = [129, 96] class = Not Popular 1062->1070 1064 entropy = 1.0 samples = 12 value = [6, 6] class = Not Popular 1063->1064 1065 Artist_Followers ≤ 0.001 entropy = 0.767 samples = 67 value = [52, 15] class = Not Popular 1063->1065 1066 Artist_Followers ≤ 0.001 entropy = 0.821 samples = 39 value = [29, 10] class = Not Popular 1065->1066 1069 entropy = 0.677 samples = 28 value = [23, 5] class = Not Popular 1065->1069 1067 entropy = 0.684 samples = 22 value = [18, 4] class = Not Popular 1066->1067 1068 entropy = 0.937 samples = 17 value = [11, 6] class = Not Popular 1066->1068 1071 Artist_Followers ≤ 0.001 entropy = 0.998 samples = 177 value = [93, 84] class = Not Popular 1070->1071 1090 Artist_Popularity ≤ 0.421 entropy = 0.811 samples = 48 value = [36, 12] class = Not Popular 1070->1090 1072 Artist_Popularity ≤ 0.357 entropy = 0.98 samples = 103 value = [60, 43] class = Not Popular 1071->1072 1081 Artist_Followers ≤ 0.001 entropy = 0.992 samples = 74 value = [33, 41] class = Popular 1071->1081 1073 Artist_Followers ≤ 0.001 entropy = 0.994 samples = 53 value = [29, 24] class = Not Popular 1072->1073 1078 Artist_Popularity ≤ 0.373 entropy = 0.958 samples = 50 value = [31, 19] class = Not Popular 1072->1078 1074 Artist_Followers ≤ 0.001 entropy = 0.971 samples = 35 value = [21, 14] class = Not Popular 1073->1074 1077 entropy = 0.991 samples = 18 value = [8, 10] class = Popular 1073->1077 1075 entropy = 0.811 samples = 4 value = [3, 1] class = Not Popular 1074->1075 1076 entropy = 0.981 samples = 31 value = [18, 13] class = Not Popular 1074->1076 1079 entropy = 0.896 samples = 16 value = [11, 5] class = Not Popular 1078->1079 1080 entropy = 0.977 samples = 34 value = [20, 14] class = Not Popular 1078->1080 1082 Artist_Followers ≤ 0.001 entropy = 0.881 samples = 40 value = [12, 28] class = Popular 1081->1082 1087 Artist_Followers ≤ 0.001 entropy = 0.96 samples = 34 value = [21, 13] class = Not Popular 1081->1087 1083 Artist_Followers ≤ 0.001 entropy = 0.961 samples = 26 value = [10, 16] class = Popular 1082->1083 1086 entropy = 0.592 samples = 14 value = [2, 12] class = Popular 1082->1086 1084 entropy = 0.896 samples = 16 value = [5, 11] class = Popular 1083->1084 1085 entropy = 1.0 samples = 10 value = [5, 5] class = Not Popular 1083->1085 1088 entropy = 0.94 samples = 14 value = [9, 5] class = Not Popular 1087->1088 1089 entropy = 0.971 samples = 20 value = [12, 8] class = Not Popular 1087->1089 1091 Artist_Followers ≤ 0.001 entropy = 0.731 samples = 44 value = [35, 9] class = Not Popular 1090->1091 1096 entropy = 0.811 samples = 4 value = [1, 3] class = Popular 1090->1096 1092 Artist_Followers ≤ 0.001 entropy = 0.579 samples = 29 value = [25, 4] class = Not Popular 1091->1092 1095 entropy = 0.918 samples = 15 value = [10, 5] class = Not Popular 1091->1095 1093 entropy = 0.667 samples = 23 value = [19, 4] class = Not Popular 1092->1093 1094 entropy = 0.0 samples = 6 value = [6, 0] class = Not Popular 1092->1094 1099 Artist_Followers ≤ 0.01 entropy = 0.909 samples = 2483 value = [1677, 806] class = Not Popular 1098->1099 1262 Artist_Followers ≤ 0.016 entropy = 0.853 samples = 1533 value = [1107, 426] class = Not Popular 1098->1262 1100 Artist_Popularity ≤ 0.421 entropy = 0.895 samples = 2333 value = [1607, 726] class = Not Popular 1099->1100 1255 Artist_Followers ≤ 0.01 entropy = 0.997 samples = 150 value = [70, 80] class = Popular 1099->1255 1101 Artist_Followers ≤ 0.008 entropy = 0.882 samples = 2181 value = [1525, 656] class = Not Popular 1100->1101 1246 Artist_Followers ≤ 0.007 entropy = 0.995 samples = 152 value = [82, 70] class = Not Popular 1100->1246 1102 Artist_Followers ≤ 0.008 entropy = 0.896 samples = 1881 value = [1294, 587] class = Not Popular 1101->1102 1227 Artist_Followers ≤ 0.01 entropy = 0.778 samples = 300 value = [231, 69] class = Not Popular 1101->1227 1103 Artist_Followers ≤ 0.007 entropy = 0.88 samples = 1743 value = [1222, 521] class = Not Popular 1102->1103 1218 Artist_Popularity ≤ 0.381 entropy = 0.999 samples = 138 value = [72, 66] class = Not Popular 1102->1218 1104 Artist_Followers ≤ 0.007 entropy = 0.895 samples = 1578 value = [1087, 491] class = Not Popular 1103->1104 1213 Artist_Followers ≤ 0.008 entropy = 0.684 samples = 165 value = [135, 30] class = Not Popular 1103->1213 1105 Artist_Popularity ≤ 0.373 entropy = 0.88 samples = 1524 value = [1068, 456] class = Not Popular 1104->1105 1210 Artist_Followers ≤ 0.007 entropy = 0.936 samples = 54 value = [19, 35] class = Popular 1104->1210 1106 Artist_Followers ≤ 0.005 entropy = 0.932 samples = 652 value = [425, 227] class = Not Popular 1105->1106 1155 Artist_Followers ≤ 0.005 entropy = 0.831 samples = 872 value = [643, 229] class = Not Popular 1105->1155 1107 Artist_Followers ≤ 0.002 entropy = 0.974 samples = 412 value = [245, 167] class = Not Popular 1106->1107 1142 Artist_Popularity ≤ 0.357 entropy = 0.811 samples = 240 value = [180, 60] class = Not Popular 1106->1142 1108 Artist_Popularity ≤ 0.341 entropy = 0.818 samples = 122 value = [91, 31] class = Not Popular 1107->1108 1115 Artist_Followers ≤ 0.004 entropy = 0.997 samples = 290 value = [154, 136] class = Not Popular 1107->1115 1109 entropy = 0.722 samples = 35 value = [28, 7] class = Not Popular 1108->1109 1110 Artist_Followers ≤ 0.002 entropy = 0.85 samples = 87 value = [63, 24] class = Not Popular 1108->1110 1111 Artist_Popularity ≤ 0.357 entropy = 0.866 samples = 59 value = [42, 17] class = Not Popular 1110->1111 1114 entropy = 0.811 samples = 28 value = [21, 7] class = Not Popular 1110->1114 1112 entropy = 0.868 samples = 38 value = [27, 11] class = Not Popular 1111->1112 1113 entropy = 0.863 samples = 21 value = [15, 6] class = Not Popular 1111->1113 1116 Artist_Followers ≤ 0.004 entropy = 0.979 samples = 183 value = [107, 76] class = Not Popular 1115->1116 1131 Artist_Popularity ≤ 0.357 entropy = 0.989 samples = 107 value = [47, 60] class = Popular 1115->1131 1117 Artist_Followers ≤ 0.003 entropy = 0.999 samples = 125 value = [65, 60] class = Not Popular 1116->1117 1128 Artist_Popularity ≤ 0.341 entropy = 0.85 samples = 58 value = [42, 16] class = Not Popular 1116->1128 1118 Artist_Followers ≤ 0.003 entropy = 0.972 samples = 82 value = [49, 33] class = Not Popular 1117->1118 1125 Artist_Popularity ≤ 0.341 entropy = 0.952 samples = 43 value = [16, 27] class = Popular 1117->1125 1119 Artist_Followers ≤ 0.003 entropy = 0.994 samples = 57 value = [31, 26] class = Not Popular 1118->1119 1124 entropy = 0.855 samples = 25 value = [18, 7] class = Not Popular 1118->1124 1120 Artist_Followers ≤ 0.003 entropy = 0.99 samples = 50 value = [28, 22] class = Not Popular 1119->1120 1123 entropy = 0.985 samples = 7 value = [3, 4] class = Popular 1119->1123 1121 entropy = 0.982 samples = 19 value = [11, 8] class = Not Popular 1120->1121 1122 entropy = 0.993 samples = 31 value = [17, 14] class = Not Popular 1120->1122 1126 entropy = 0.961 samples = 26 value = [10, 16] class = Popular 1125->1126 1127 entropy = 0.937 samples = 17 value = [6, 11] class = Popular 1125->1127 1129 entropy = 0.908 samples = 34 value = [23, 11] class = Not Popular 1128->1129 1130 entropy = 0.738 samples = 24 value = [19, 5] class = Not Popular 1128->1130 1132 Artist_Followers ≤ 0.004 entropy = 0.999 samples = 98 value = [47, 51] class = Popular 1131->1132 1141 entropy = 0.0 samples = 9 value = [0, 9] class = Popular 1131->1141 1133 entropy = 0.503 samples = 9 value = [1, 8] class = Popular 1132->1133 1134 Artist_Followers ≤ 0.005 entropy = 0.999 samples = 89 value = [46, 43] class = Not Popular 1132->1134 1135 Artist_Followers ≤ 0.004 entropy = 0.999 samples = 56 value = [27, 29] class = Popular 1134->1135 1140 entropy = 0.983 samples = 33 value = [19, 14] class = Not Popular 1134->1140 1136 entropy = 0.994 samples = 22 value = [10, 12] class = Popular 1135->1136 1137 Artist_Followers ≤ 0.005 entropy = 1.0 samples = 34 value = [17, 17] class = Not Popular 1135->1137 1138 entropy = 0.998 samples = 17 value = [9, 8] class = Not Popular 1137->1138 1139 entropy = 0.998 samples = 17 value = [8, 9] class = Popular 1137->1139 1143 Artist_Followers ≤ 0.005 entropy = 0.742 samples = 228 value = [180, 48] class = Not Popular 1142->1143 1154 entropy = 0.0 samples = 12 value = [0, 12] class = Popular 1142->1154 1144 entropy = 0.498 samples = 64 value = [57, 7] class = Not Popular 1143->1144 1145 Artist_Followers ≤ 0.006 entropy = 0.811 samples = 164 value = [123, 41] class = Not Popular 1143->1145 1146 Artist_Followers ≤ 0.006 entropy = 0.863 samples = 126 value = [90, 36] class = Not Popular 1145->1146 1153 entropy = 0.562 samples = 38 value = [33, 5] class = Not Popular 1145->1153 1147 Artist_Popularity ≤ 0.341 entropy = 0.776 samples = 105 value = [81, 24] class = Not Popular 1146->1147 1152 entropy = 0.985 samples = 21 value = [9, 12] class = Popular 1146->1152 1148 entropy = 0.696 samples = 32 value = [26, 6] class = Not Popular 1147->1148 1149 Artist_Followers ≤ 0.006 entropy = 0.806 samples = 73 value = [55, 18] class = Not Popular 1147->1149 1150 entropy = 0.878 samples = 37 value = [26, 11] class = Not Popular 1149->1150 1151 entropy = 0.711 samples = 36 value = [29, 7] class = Not Popular 1149->1151 1156 Artist_Followers ≤ 0.003 entropy = 0.857 samples = 558 value = [401, 157] class = Not Popular 1155->1156 1193 Artist_Followers ≤ 0.005 entropy = 0.777 samples = 314 value = [242, 72] class = Not Popular 1155->1193 1157 Artist_Followers ≤ 0.002 entropy = 0.794 samples = 334 value = [254, 80] class = Not Popular 1156->1157 1178 Artist_Followers ≤ 0.003 entropy = 0.928 samples = 224 value = [147, 77] class = Not Popular 1156->1178 1158 Artist_Followers ≤ 0.002 entropy = 0.891 samples = 120 value = [83, 37] class = Not Popular 1157->1158 1165 Artist_Followers ≤ 0.002 entropy = 0.724 samples = 214 value = [171, 43] class = Not Popular 1157->1165 1159 Artist_Followers ≤ 0.002 entropy = 0.837 samples = 90 value = [66, 24] class = Not Popular 1158->1159 1164 entropy = 0.987 samples = 30 value = [17, 13] class = Not Popular 1158->1164 1160 Artist_Popularity ≤ 0.397 entropy = 0.866 samples = 66 value = [47, 19] class = Not Popular 1159->1160 1163 entropy = 0.738 samples = 24 value = [19, 5] class = Not Popular 1159->1163 1161 entropy = 0.869 samples = 31 value = [22, 9] class = Not Popular 1160->1161 1162 entropy = 0.863 samples = 35 value = [25, 10] class = Not Popular 1160->1162 1166 Artist_Popularity ≤ 0.405 entropy = 0.491 samples = 56 value = [50, 6] class = Not Popular 1165->1166 1169 Artist_Followers ≤ 0.003 entropy = 0.785 samples = 158 value = [121, 37] class = Not Popular 1165->1169 1167 entropy = 0.469 samples = 20 value = [18, 2] class = Not Popular 1166->1167 1168 entropy = 0.503 samples = 36 value = [32, 4] class = Not Popular 1166->1168 1170 Artist_Followers ≤ 0.002 entropy = 0.821 samples = 125 value = [93, 32] class = Not Popular 1169->1170 1177 entropy = 0.614 samples = 33 value = [28, 5] class = Not Popular 1169->1177 1171 entropy = 0.737 samples = 53 value = [42, 11] class = Not Popular 1170->1171 1172 Artist_Followers ≤ 0.002 entropy = 0.871 samples = 72 value = [51, 21] class = Not Popular 1170->1172 1173 entropy = 0.997 samples = 15 value = [8, 7] class = Not Popular 1172->1173 1174 Artist_Followers ≤ 0.003 entropy = 0.804 samples = 57 value = [43, 14] class = Not Popular 1172->1174 1175 entropy = 0.758 samples = 32 value = [25, 7] class = Not Popular 1174->1175 1176 entropy = 0.855 samples = 25 value = [18, 7] class = Not Popular 1174->1176 1179 entropy = 0.837 samples = 15 value = [4, 11] class = Popular 1178->1179 1180 Artist_Popularity ≤ 0.405 entropy = 0.9 samples = 209 value = [143, 66] class = Not Popular 1178->1180 1181 Artist_Followers ≤ 0.004 entropy = 0.923 samples = 142 value = [94, 48] class = Not Popular 1180->1181 1190 Artist_Followers ≤ 0.004 entropy = 0.84 samples = 67 value = [49, 18] class = Not Popular 1180->1190 1182 entropy = 0.998 samples = 17 value = [9, 8] class = Not Popular 1181->1182 1183 Artist_Followers ≤ 0.004 entropy = 0.904 samples = 125 value = [85, 40] class = Not Popular 1181->1183 1184 entropy = 0.784 samples = 30 value = [23, 7] class = Not Popular 1183->1184 1185 Artist_Followers ≤ 0.004 entropy = 0.932 samples = 95 value = [62, 33] class = Not Popular 1183->1185 1186 entropy = 0.971 samples = 35 value = [21, 14] class = Not Popular 1185->1186 1187 Artist_Followers ≤ 0.004 entropy = 0.901 samples = 60 value = [41, 19] class = Not Popular 1185->1187 1188 entropy = 0.904 samples = 25 value = [17, 8] class = Not Popular 1187->1188 1189 entropy = 0.898 samples = 35 value = [24, 11] class = Not Popular 1187->1189 1191 entropy = 0.849 samples = 40 value = [29, 11] class = Not Popular 1190->1191 1192 entropy = 0.826 samples = 27 value = [20, 7] class = Not Popular 1190->1192 1194 Artist_Followers ≤ 0.005 entropy = 0.655 samples = 71 value = [59, 12] class = Not Popular 1193->1194 1197 Artist_Popularity ≤ 0.405 entropy = 0.806 samples = 243 value = [183, 60] class = Not Popular 1193->1197 1195 entropy = 0.787 samples = 17 value = [13, 4] class = Not Popular 1194->1195 1196 entropy = 0.605 samples = 54 value = [46, 8] class = Not Popular 1194->1196 1198 Artist_Followers ≤ 0.006 entropy = 0.854 samples = 122 value = [88, 34] class = Not Popular 1197->1198 1203 Artist_Followers ≤ 0.005 entropy = 0.751 samples = 121 value = [95, 26] class = Not Popular 1197->1203 1199 entropy = 0.82 samples = 43 value = [32, 11] class = Not Popular 1198->1199 1200 Artist_Popularity ≤ 0.389 entropy = 0.87 samples = 79 value = [56, 23] class = Not Popular 1198->1200 1201 entropy = 0.874 samples = 34 value = [24, 10] class = Not Popular 1200->1201 1202 entropy = 0.867 samples = 45 value = [32, 13] class = Not Popular 1200->1202 1204 entropy = 0.787 samples = 34 value = [26, 8] class = Not Popular 1203->1204 1205 Artist_Followers ≤ 0.007 entropy = 0.736 samples = 87 value = [69, 18] class = Not Popular 1203->1205 1206 Artist_Followers ≤ 0.006 entropy = 0.679 samples = 39 value = [32, 7] class = Not Popular 1205->1206 1209 entropy = 0.777 samples = 48 value = [37, 11] class = Not Popular 1205->1209 1207 entropy = 0.722 samples = 25 value = [20, 5] class = Not Popular 1206->1207 1208 entropy = 0.592 samples = 14 value = [12, 2] class = Not Popular 1206->1208 1211 entropy = 0.987 samples = 44 value = [19, 25] class = Popular 1210->1211 1212 entropy = 0.0 samples = 10 value = [0, 10] class = Popular 1210->1212 1214 entropy = 0.615 samples = 46 value = [39, 7] class = Not Popular 1213->1214 1215 Artist_Followers ≤ 0.008 entropy = 0.708 samples = 119 value = [96, 23] class = Not Popular 1213->1215 1216 entropy = 0.722 samples = 50 value = [40, 10] class = Not Popular 1215->1216 1217 entropy = 0.698 samples = 69 value = [56, 13] class = Not Popular 1215->1217 1219 Artist_Popularity ≤ 0.349 entropy = 0.988 samples = 85 value = [37, 48] class = Popular 1218->1219 1224 Artist_Followers ≤ 0.008 entropy = 0.924 samples = 53 value = [35, 18] class = Not Popular 1218->1224 1220 entropy = 0.985 samples = 35 value = [20, 15] class = Not Popular 1219->1220 1221 Artist_Followers ≤ 0.008 entropy = 0.925 samples = 50 value = [17, 33] class = Popular 1219->1221 1222 entropy = 0.918 samples = 6 value = [2, 4] class = Popular 1221->1222 1223 entropy = 0.926 samples = 44 value = [15, 29] class = Popular 1221->1223 1225 entropy = 0.929 samples = 29 value = [19, 10] class = Not Popular 1224->1225 1226 entropy = 0.918 samples = 24 value = [16, 8] class = Not Popular 1224->1226 1228 Artist_Popularity ≤ 0.341 entropy = 0.757 samples = 266 value = [208, 58] class = Not Popular 1227->1228 1245 entropy = 0.908 samples = 34 value = [23, 11] class = Not Popular 1227->1245 1229 entropy = 0.503 samples = 36 value = [32, 4] class = Not Popular 1228->1229 1230 Artist_Followers ≤ 0.009 entropy = 0.786 samples = 230 value = [176, 54] class = Not Popular 1228->1230 1231 Artist_Popularity ≤ 0.397 entropy = 0.733 samples = 112 value = [89, 23] class = Not Popular 1230->1231 1238 Artist_Followers ≤ 0.009 entropy = 0.831 samples = 118 value = [87, 31] class = Not Popular 1230->1238 1232 Artist_Followers ≤ 0.009 entropy = 0.755 samples = 92 value = [72, 20] class = Not Popular 1231->1232 1237 entropy = 0.61 samples = 20 value = [17, 3] class = Not Popular 1231->1237 1233 entropy = 0.722 samples = 10 value = [8, 2] class = Not Popular 1232->1233 1234 Artist_Followers ≤ 0.009 entropy = 0.759 samples = 82 value = [64, 18] class = Not Popular 1232->1234 1235 entropy = 0.769 samples = 40 value = [31, 9] class = Not Popular 1234->1235 1236 entropy = 0.75 samples = 42 value = [33, 9] class = Not Popular 1234->1236 1239 Artist_Followers ≤ 0.009 entropy = 0.951 samples = 27 value = [17, 10] class = Not Popular 1238->1239 1242 Artist_Followers ≤ 0.01 entropy = 0.779 samples = 91 value = [70, 21] class = Not Popular 1238->1242 1240 entropy = 1.0 samples = 2 value = [1, 1] class = Not Popular 1239->1240 1241 entropy = 0.943 samples = 25 value = [16, 9] class = Not Popular 1239->1241 1243 entropy = 0.84 samples = 26 value = [19, 7] class = Not Popular 1242->1243 1244 entropy = 0.752 samples = 65 value = [51, 14] class = Not Popular 1242->1244 1247 Artist_Followers ≤ 0.004 entropy = 0.953 samples = 83 value = [52, 31] class = Not Popular 1246->1247 1252 Artist_Followers ≤ 0.009 entropy = 0.988 samples = 69 value = [30, 39] class = Popular 1246->1252 1248 entropy = 0.811 samples = 16 value = [12, 4] class = Not Popular 1247->1248 1249 Artist_Followers ≤ 0.005 entropy = 0.973 samples = 67 value = [40, 27] class = Not Popular 1247->1249 1250 entropy = 0.0 samples = 3 value = [0, 3] class = Popular 1249->1250 1251 entropy = 0.954 samples = 64 value = [40, 24] class = Not Popular 1249->1251 1253 entropy = 0.637 samples = 31 value = [5, 26] class = Popular 1252->1253 1254 entropy = 0.927 samples = 38 value = [25, 13] class = Not Popular 1252->1254 1256 Artist_Followers ≤ 0.01 entropy = 0.544 samples = 32 value = [4, 28] class = Popular 1255->1256 1259 Artist_Followers ≤ 0.01 entropy = 0.99 samples = 118 value = [66, 52] class = Not Popular 1255->1259 1257 entropy = 0.337 samples = 16 value = [1, 15] class = Popular 1256->1257 1258 entropy = 0.696 samples = 16 value = [3, 13] class = Popular 1256->1258 1260 entropy = 0.952 samples = 70 value = [44, 26] class = Not Popular 1259->1260 1261 entropy = 0.995 samples = 48 value = [22, 26] class = Popular 1259->1261 1263 Artist_Popularity ≤ 0.373 entropy = 0.817 samples = 1116 value = [833, 283] class = Not Popular 1262->1263 1304 Artist_Followers ≤ 0.018 entropy = 0.928 samples = 417 value = [274, 143] class = Not Popular 1262->1304 1264 Artist_Followers ≤ 0.011 entropy = 0.892 samples = 408 value = [282, 126] class = Not Popular 1263->1264 1281 Artist_Followers ≤ 0.01 entropy = 0.763 samples = 708 value = [551, 157] class = Not Popular 1263->1281 1265 Artist_Followers ≤ 0.011 entropy = 0.653 samples = 113 value = [94, 19] class = Not Popular 1264->1265 1268 Artist_Followers ≤ 0.014 entropy = 0.945 samples = 295 value = [188, 107] class = Not Popular 1264->1268 1266 entropy = 0.667 samples = 23 value = [19, 4] class = Not Popular 1265->1266 1267 entropy = 0.65 samples = 90 value = [75, 15] class = Not Popular 1265->1267 1269 Artist_Followers ≤ 0.011 entropy = 1.0 samples = 141 value = [72, 69] class = Not Popular 1268->1269 1276 Artist_Followers ≤ 0.015 entropy = 0.806 samples = 154 value = [116, 38] class = Not Popular 1268->1276 1270 entropy = 0.91 samples = 40 value = [13, 27] class = Popular 1269->1270 1271 Artist_Popularity ≤ 0.349 entropy = 0.979 samples = 101 value = [59, 42] class = Not Popular 1269->1271 1272 Artist_Followers ≤ 0.012 entropy = 0.994 samples = 44 value = [20, 24] class = Popular 1271->1272 1275 entropy = 0.9 samples = 57 value = [39, 18] class = Not Popular 1271->1275 1273 entropy = 0.985 samples = 21 value = [9, 12] class = Popular 1272->1273 1274 entropy = 0.999 samples = 23 value = [11, 12] class = Popular 1272->1274 1277 Artist_Followers ≤ 0.014 entropy = 0.732 samples = 117 value = [93, 24] class = Not Popular 1276->1277 1280 entropy = 0.957 samples = 37 value = [23, 14] class = Not Popular 1276->1280 1278 entropy = 0.627 samples = 51 value = [43, 8] class = Not Popular 1277->1278 1279 entropy = 0.799 samples = 66 value = [50, 16] class = Not Popular 1277->1279 1282 entropy = 0.55 samples = 55 value = [48, 7] class = Not Popular 1281->1282 1283 Artist_Followers ≤ 0.013 entropy = 0.778 samples = 653 value = [503, 150] class = Not Popular 1281->1283 1284 Artist_Followers ≤ 0.012 entropy = 0.847 samples = 208 value = [151, 57] class = Not Popular 1283->1284 1291 Artist_Popularity ≤ 0.389 entropy = 0.74 samples = 445 value = [352, 93] class = Not Popular 1283->1291 1285 Artist_Followers ≤ 0.011 entropy = 0.82 samples = 137 value = [102, 35] class = Not Popular 1284->1285 1290 entropy = 0.893 samples = 71 value = [49, 22] class = Not Popular 1284->1290 1286 entropy = 0.9 samples = 38 value = [26, 12] class = Not Popular 1285->1286 1287 Artist_Followers ≤ 0.012 entropy = 0.782 samples = 99 value = [76, 23] class = Not Popular 1285->1287 1288 entropy = 0.75 samples = 42 value = [33, 9] class = Not Popular 1287->1288 1289 entropy = 0.804 samples = 57 value = [43, 14] class = Not Popular 1287->1289 1292 Artist_Followers ≤ 0.014 entropy = 0.879 samples = 104 value = [73, 31] class = Not Popular 1291->1292 1295 Artist_Followers ≤ 0.014 entropy = 0.684 samples = 341 value = [279, 62] class = Not Popular 1291->1295 1293 entropy = 0.722 samples = 70 value = [56, 14] class = Not Popular 1292->1293 1294 entropy = 1.0 samples = 34 value = [17, 17] class = Not Popular 1292->1294 1296 Artist_Followers ≤ 0.013 entropy = 0.754 samples = 203 value = [159, 44] class = Not Popular 1295->1296 1301 Artist_Popularity ≤ 0.413 entropy = 0.559 samples = 138 value = [120, 18] class = Not Popular 1295->1301 1297 Artist_Popularity ≤ 0.421 entropy = 0.807 samples = 97 value = [73, 24] class = Not Popular 1296->1297 1300 entropy = 0.699 samples = 106 value = [86, 20] class = Not Popular 1296->1300 1298 entropy = 0.702 samples = 84 value = [68, 16] class = Not Popular 1297->1298 1299 entropy = 0.961 samples = 13 value = [5, 8] class = Popular 1297->1299 1302 entropy = 0.642 samples = 92 value = [77, 15] class = Not Popular 1301->1302 1303 entropy = 0.348 samples = 46 value = [43, 3] class = Not Popular 1301->1303 1305 Artist_Followers ≤ 0.016 entropy = 0.984 samples = 232 value = [133, 99] class = Not Popular 1304->1305 1316 Artist_Followers ≤ 0.018 entropy = 0.791 samples = 185 value = [141, 44] class = Not Popular 1304->1316 1306 entropy = 0.948 samples = 30 value = [11, 19] class = Popular 1305->1306 1307 Artist_Followers ≤ 0.018 entropy = 0.969 samples = 202 value = [122, 80] class = Not Popular 1305->1307 1308 Artist_Followers ≤ 0.017 entropy = 0.928 samples = 160 value = [105, 55] class = Not Popular 1307->1308 1315 entropy = 0.974 samples = 42 value = [17, 25] class = Popular 1307->1315 1309 Artist_Followers ≤ 0.016 entropy = 0.88 samples = 77 value = [54, 23] class = Not Popular 1308->1309 1312 Artist_Popularity ≤ 0.397 entropy = 0.962 samples = 83 value = [51, 32] class = Not Popular 1308->1312 1310 entropy = 0.881 samples = 20 value = [14, 6] class = Not Popular 1309->1310 1311 entropy = 0.879 samples = 57 value = [40, 17] class = Not Popular 1309->1311 1313 entropy = 0.947 samples = 41 value = [26, 15] class = Not Popular 1312->1313 1314 entropy = 0.974 samples = 42 value = [25, 17] class = Not Popular 1312->1314 1317 entropy = 0.656 samples = 65 value = [54, 11] class = Not Popular 1316->1317 1318 Artist_Popularity ≤ 0.389 entropy = 0.849 samples = 120 value = [87, 33] class = Not Popular 1316->1318 1319 entropy = 0.999 samples = 25 value = [12, 13] class = Popular 1318->1319 1320 Artist_Followers ≤ 0.018 entropy = 0.742 samples = 95 value = [75, 20] class = Not Popular 1318->1320 1321 entropy = 0.785 samples = 47 value = [36, 11] class = Not Popular 1320->1321 1322 entropy = 0.696 samples = 48 value = [39, 9] class = Not Popular 1320->1322 1324 entropy = 0.773 samples = 22 value = [5, 17] class = Popular 1323->1324 1325 Artist_Followers ≤ 0.019 entropy = 0.972 samples = 615 value = [368, 247] class = Not Popular 1323->1325 1326 entropy = 0.752 samples = 51 value = [40, 11] class = Not Popular 1325->1326 1327 Artist_Popularity ≤ 0.365 entropy = 0.981 samples = 564 value = [328, 236] class = Not Popular 1325->1327 1328 entropy = 0.0 samples = 5 value = [0, 5] class = Popular 1327->1328 1329 Artist_Followers ≤ 0.021 entropy = 0.978 samples = 559 value = [328, 231] class = Not Popular 1327->1329 1330 entropy = 0.999 samples = 87 value = [42, 45] class = Popular 1329->1330 1331 Artist_Followers ≤ 0.027 entropy = 0.967 samples = 472 value = [286, 186] class = Not Popular 1329->1331 1332 Artist_Popularity ≤ 0.413 entropy = 0.912 samples = 205 value = [138, 67] class = Not Popular 1331->1332 1337 Artist_Followers ≤ 0.029 entropy = 0.991 samples = 267 value = [148, 119] class = Not Popular 1331->1337 1333 entropy = 0.829 samples = 65 value = [48, 17] class = Not Popular 1332->1333 1334 Artist_Followers ≤ 0.025 entropy = 0.94 samples = 140 value = [90, 50] class = Not Popular 1332->1334 1335 entropy = 0.999 samples = 48 value = [25, 23] class = Not Popular 1334->1335 1336 entropy = 0.873 samples = 92 value = [65, 27] class = Not Popular 1334->1336 1338 Artist_Popularity ≤ 0.405 entropy = 0.99 samples = 75 value = [33, 42] class = Popular 1337->1338 1341 Artist_Followers ≤ 0.031 entropy = 0.972 samples = 192 value = [115, 77] class = Not Popular 1337->1341 1339 entropy = 0.0 samples = 7 value = [0, 7] class = Popular 1338->1339 1340 entropy = 0.999 samples = 68 value = [33, 35] class = Popular 1338->1340 1342 entropy = 0.773 samples = 44 value = [34, 10] class = Not Popular 1341->1342 1343 Artist_Followers ≤ 0.033 entropy = 0.994 samples = 148 value = [81, 67] class = Not Popular 1341->1343 1344 entropy = 0.958 samples = 58 value = [36, 22] class = Not Popular 1343->1344 1345 Artist_Followers ≤ 0.035 entropy = 1.0 samples = 90 value = [45, 45] class = Not Popular 1343->1345 1346 entropy = 0.918 samples = 36 value = [12, 24] class = Popular 1345->1346 1347 entropy = 0.964 samples = 54 value = [33, 21] class = Not Popular 1345->1347 1349 Artist_Followers ≤ 0.041 entropy = 0.98 samples = 5592 value = [3261, 2331] class = Not Popular 1348->1349 1588 Artist_Popularity ≤ 0.516 entropy = 0.777 samples = 288 value = [222, 66] class = Not Popular 1348->1588 1350 Artist_Followers ≤ 0.011 entropy = 0.976 samples = 5350 value = [3159, 2191] class = Not Popular 1349->1350 1577 Artist_Popularity ≤ 0.516 entropy = 0.982 samples = 242 value = [102, 140] class = Popular 1349->1577 1351 Artist_Followers ≤ 0.01 entropy = 0.947 samples = 1594 value = [1011, 583] class = Not Popular 1350->1351 1438 Artist_Followers ≤ 0.023 entropy = 0.985 samples = 3756 value = [2148, 1608] class = Not Popular 1350->1438 1352 Artist_Followers ≤ 0.003 entropy = 0.983 samples = 1043 value = [602, 441] class = Not Popular 1351->1352 1423 Artist_Popularity ≤ 0.452 entropy = 0.823 samples = 551 value = [409, 142] class = Not Popular 1351->1423 1353 Artist_Followers ≤ 0.001 entropy = 0.883 samples = 315 value = [220, 95] class = Not Popular 1352->1353 1374 Artist_Followers ≤ 0.004 entropy = 0.998 samples = 728 value = [382, 346] class = Not Popular 1352->1374 1354 Artist_Followers ≤ 0.0 entropy = 0.97 samples = 88 value = [53, 35] class = Not Popular 1353->1354 1359 Artist_Followers ≤ 0.002 entropy = 0.833 samples = 227 value = [167, 60] class = Not Popular 1353->1359 1355 entropy = 0.985 samples = 7 value = [3, 4] class = Popular 1354->1355 1356 Artist_Followers ≤ 0.001 entropy = 0.96 samples = 81 value = [50, 31] class = Not Popular 1354->1356 1357 entropy = 0.934 samples = 20 value = [13, 7] class = Not Popular 1356->1357 1358 entropy = 0.967 samples = 61 value = [37, 24] class = Not Popular 1356->1358 1360 Artist_Followers ≤ 0.002 entropy = 0.525 samples = 76 value = [67, 9] class = Not Popular 1359->1360 1363 Artist_Followers ≤ 0.002 entropy = 0.923 samples = 151 value = [100, 51] class = Not Popular 1359->1363 1361 entropy = 0.771 samples = 31 value = [24, 7] class = Not Popular 1360->1361 1362 entropy = 0.262 samples = 45 value = [43, 2] class = Not Popular 1360->1362 1364 Artist_Followers ≤ 0.002 entropy = 0.947 samples = 93 value = [59, 34] class = Not Popular 1363->1364 1371 Artist_Popularity ≤ 0.476 entropy = 0.873 samples = 58 value = [41, 17] class = Not Popular 1363->1371 1365 entropy = 0.9 samples = 19 value = [13, 6] class = Not Popular 1364->1365 1366 Artist_Popularity ≤ 0.46 entropy = 0.957 samples = 74 value = [46, 28] class = Not Popular 1364->1366 1367 entropy = 0.983 samples = 26 value = [15, 11] class = Not Popular 1366->1367 1368 Artist_Popularity ≤ 0.492 entropy = 0.938 samples = 48 value = [31, 17] class = Not Popular 1366->1368 1369 entropy = 0.946 samples = 22 value = [14, 8] class = Not Popular 1368->1369 1370 entropy = 0.931 samples = 26 value = [17, 9] class = Not Popular 1368->1370 1372 entropy = 0.784 samples = 30 value = [23, 7] class = Not Popular 1371->1372 1373 entropy = 0.94 samples = 28 value = [18, 10] class = Not Popular 1371->1373 1375 Artist_Followers ≤ 0.004 entropy = 0.781 samples = 82 value = [19, 63] class = Popular 1374->1375 1382 Artist_Popularity ≤ 0.484 entropy = 0.989 samples = 646 value = [363, 283] class = Not Popular 1374->1382 1376 entropy = 1.0 samples = 26 value = [13, 13] class = Not Popular 1375->1376 1377 Artist_Popularity ≤ 0.516 entropy = 0.491 samples = 56 value = [6, 50] class = Popular 1375->1377 1378 Artist_Followers ≤ 0.004 entropy = 0.292 samples = 39 value = [2, 37] class = Popular 1377->1378 1381 entropy = 0.787 samples = 17 value = [4, 13] class = Popular 1377->1381 1379 entropy = 0.323 samples = 17 value = [1, 16] class = Popular 1378->1379 1380 entropy = 0.267 samples = 22 value = [1, 21] class = Popular 1378->1380 1383 Artist_Followers ≤ 0.009 entropy = 0.998 samples = 391 value = [185, 206] class = Popular 1382->1383 1410 Artist_Followers ≤ 0.006 entropy = 0.884 samples = 255 value = [178, 77] class = Not Popular 1382->1410 1384 Artist_Followers ≤ 0.008 entropy = 0.999 samples = 382 value = [185, 197] class = Popular 1383->1384 1409 entropy = 0.0 samples = 9 value = [0, 9] class = Popular 1383->1409 1385 Artist_Followers ≤ 0.005 entropy = 0.979 samples = 219 value = [91, 128] class = Popular 1384->1385 1400 Artist_Followers ≤ 0.008 entropy = 0.983 samples = 163 value = [94, 69] class = Not Popular 1384->1400 1386 entropy = 0.0 samples = 6 value = [0, 6] class = Popular 1385->1386 1387 Artist_Followers ≤ 0.005 entropy = 0.985 samples = 213 value = [91, 122] class = Popular 1385->1387 1388 Artist_Followers ≤ 0.005 entropy = 0.999 samples = 105 value = [54, 51] class = Not Popular 1387->1388 1393 Artist_Followers ≤ 0.006 entropy = 0.927 samples = 108 value = [37, 71] class = Popular 1387->1393 1389 entropy = 0.779 samples = 13 value = [3, 10] class = Popular 1388->1389 1390 Artist_Popularity ≤ 0.468 entropy = 0.991 samples = 92 value = [51, 41] class = Not Popular 1388->1390 1391 entropy = 0.996 samples = 43 value = [23, 20] class = Not Popular 1390->1391 1392 entropy = 0.985 samples = 49 value = [28, 21] class = Not Popular 1390->1392 1394 entropy = 0.787 samples = 34 value = [8, 26] class = Popular 1393->1394 1395 Artist_Followers ≤ 0.008 entropy = 0.966 samples = 74 value = [29, 45] class = Popular 1393->1395 1396 Artist_Followers ≤ 0.007 entropy = 0.961 samples = 65 value = [25, 40] class = Popular 1395->1396 1399 entropy = 0.991 samples = 9 value = [4, 5] class = Popular 1395->1399 1397 entropy = 0.974 samples = 42 value = [17, 25] class = Popular 1396->1397 1398 entropy = 0.932 samples = 23 value = [8, 15] class = Popular 1396->1398 1401 entropy = 0.706 samples = 52 value = [42, 10] class = Not Popular 1400->1401 1402 Artist_Popularity ≤ 0.468 entropy = 0.997 samples = 111 value = [52, 59] class = Popular 1400->1402 1403 Artist_Followers ≤ 0.009 entropy = 0.942 samples = 64 value = [23, 41] class = Popular 1402->1403 1408 entropy = 0.96 samples = 47 value = [29, 18] class = Not Popular 1402->1408 1404 Artist_Followers ≤ 0.009 entropy = 1.0 samples = 40 value = [20, 20] class = Not Popular 1403->1404 1407 entropy = 0.544 samples = 24 value = [3, 21] class = Popular 1403->1407 1405 entropy = 0.961 samples = 13 value = [5, 8] class = Popular 1404->1405 1406 entropy = 0.991 samples = 27 value = [15, 12] class = Not Popular 1404->1406 1411 Artist_Followers ≤ 0.005 entropy = 0.576 samples = 139 value = [120, 19] class = Not Popular 1410->1411 1416 Artist_Popularity ≤ 0.516 entropy = 1.0 samples = 116 value = [58, 58] class = Not Popular 1410->1416 1412 entropy = 0.477 samples = 39 value = [35, 4] class = Not Popular 1411->1412 1413 Artist_Followers ≤ 0.005 entropy = 0.61 samples = 100 value = [85, 15] class = Not Popular 1411->1413 1414 entropy = 0.657 samples = 59 value = [49, 10] class = Not Popular 1413->1414 1415 entropy = 0.535 samples = 41 value = [36, 5] class = Not Popular 1413->1415 1417 Artist_Followers ≤ 0.008 entropy = 0.879 samples = 67 value = [20, 47] class = Popular 1416->1417 1422 entropy = 0.768 samples = 49 value = [38, 11] class = Not Popular 1416->1422 1418 Artist_Followers ≤ 0.007 entropy = 0.946 samples = 55 value = [20, 35] class = Popular 1417->1418 1421 entropy = 0.0 samples = 12 value = [0, 12] class = Popular 1417->1421 1419 entropy = 0.987 samples = 30 value = [13, 17] class = Popular 1418->1419 1420 entropy = 0.855 samples = 25 value = [7, 18] class = Popular 1418->1420 1424 Artist_Followers ≤ 0.01 entropy = 0.711 samples = 113 value = [91, 22] class = Not Popular 1423->1424 1427 Artist_Followers ≤ 0.01 entropy = 0.847 samples = 438 value = [318, 120] class = Not Popular 1423->1427 1425 entropy = 0.475 samples = 49 value = [44, 5] class = Not Popular 1424->1425 1426 entropy = 0.835 samples = 64 value = [47, 17] class = Not Popular 1424->1426 1428 Artist_Followers ≤ 0.01 entropy = 0.734 samples = 131 value = [104, 27] class = Not Popular 1427->1428 1431 Artist_Popularity ≤ 0.468 entropy = 0.885 samples = 307 value = [214, 93] class = Not Popular 1427->1431 1429 entropy = 0.619 samples = 39 value = [33, 6] class = Not Popular 1428->1429 1430 entropy = 0.775 samples = 92 value = [71, 21] class = Not Popular 1428->1430 1432 Artist_Followers ≤ 0.01 entropy = 0.979 samples = 94 value = [55, 39] class = Not Popular 1431->1432 1435 Artist_Popularity ≤ 0.484 entropy = 0.817 samples = 213 value = [159, 54] class = Not Popular 1431->1435 1433 entropy = 0.992 samples = 58 value = [32, 26] class = Not Popular 1432->1433 1434 entropy = 0.944 samples = 36 value = [23, 13] class = Not Popular 1432->1434 1436 entropy = 0.818 samples = 126 value = [94, 32] class = Not Popular 1435->1436 1437 entropy = 0.816 samples = 87 value = [65, 22] class = Not Popular 1435->1437 1439 Artist_Followers ≤ 0.021 entropy = 0.997 samples = 2368 value = [1264, 1104] class = Not Popular 1438->1439 1540 Artist_Followers ≤ 0.03 entropy = 0.945 samples = 1388 value = [884, 504] class = Not Popular 1438->1540 1440 Artist_Followers ≤ 0.012 entropy = 0.992 samples = 2010 value = [1112, 898] class = Not Popular 1439->1440 1521 Artist_Followers ≤ 0.022 entropy = 0.984 samples = 358 value = [152, 206] class = Popular 1439->1521 1441 Artist_Followers ≤ 0.012 entropy = 0.994 samples = 232 value = [105, 127] class = Popular 1440->1441 1452 Artist_Followers ≤ 0.013 entropy = 0.987 samples = 1778 value = [1007, 771] class = Not Popular 1440->1452 1442 Artist_Followers ≤ 0.012 entropy = 0.996 samples = 180 value = [97, 83] class = Not Popular 1441->1442 1449 Artist_Followers ≤ 0.012 entropy = 0.619 samples = 52 value = [8, 44] class = Popular 1441->1449 1443 Artist_Followers ≤ 0.012 entropy = 0.996 samples = 129 value = [60, 69] class = Popular 1442->1443 1448 entropy = 0.848 samples = 51 value = [37, 14] class = Not Popular 1442->1448 1444 entropy = 0.953 samples = 51 value = [19, 32] class = Popular 1443->1444 1445 Artist_Popularity ≤ 0.476 entropy = 0.998 samples = 78 value = [41, 37] class = Not Popular 1443->1445 1446 entropy = 1.0 samples = 44 value = [22, 22] class = Not Popular 1445->1446 1447 entropy = 0.99 samples = 34 value = [19, 15] class = Not Popular 1445->1447 1450 entropy = 0.722 samples = 25 value = [5, 20] class = Popular 1449->1450 1451 entropy = 0.503 samples = 27 value = [3, 24] class = Popular 1449->1451 1453 Artist_Followers ≤ 0.013 entropy = 0.838 samples = 112 value = [82, 30] class = Not Popular 1452->1453 1458 Artist_Followers ≤ 0.013 entropy = 0.991 samples = 1666 value = [925, 741] class = Not Popular 1452->1458 1454 Artist_Popularity ≤ 0.468 entropy = 0.986 samples = 58 value = [33, 25] class = Not Popular 1453->1454 1457 entropy = 0.445 samples = 54 value = [49, 5] class = Not Popular 1453->1457 1455 entropy = 0.954 samples = 16 value = [10, 6] class = Not Popular 1454->1455 1456 entropy = 0.993 samples = 42 value = [23, 19] class = Not Popular 1454->1456 1459 entropy = 0.877 samples = 27 value = [8, 19] class = Popular 1458->1459 1460 Artist_Followers ≤ 0.014 entropy = 0.99 samples = 1639 value = [917, 722] class = Not Popular 1458->1460 1461 Artist_Followers ≤ 0.014 entropy = 0.964 samples = 391 value = [239, 152] class = Not Popular 1460->1461 1474 Artist_Followers ≤ 0.014 entropy = 0.995 samples = 1248 value = [678, 570] class = Not Popular 1460->1474 1462 Artist_Followers ≤ 0.014 entropy = 0.999 samples = 254 value = [131, 123] class = Not Popular 1461->1462 1471 Artist_Popularity ≤ 0.508 entropy = 0.745 samples = 137 value = [108, 29] class = Not Popular 1461->1471 1463 Artist_Followers ≤ 0.013 entropy = 0.981 samples = 210 value = [122, 88] class = Not Popular 1462->1463 1470 entropy = 0.731 samples = 44 value = [9, 35] class = Popular 1462->1470 1464 Artist_Followers ≤ 0.013 entropy = 1.0 samples = 155 value = [79, 76] class = Not Popular 1463->1464 1469 entropy = 0.757 samples = 55 value = [43, 12] class = Not Popular 1463->1469 1465 Artist_Followers ≤ 0.013 entropy = 1.0 samples = 98 value = [49, 49] class = Not Popular 1464->1465 1468 entropy = 0.998 samples = 57 value = [30, 27] class = Not Popular 1464->1468 1466 entropy = 0.999 samples = 48 value = [25, 23] class = Not Popular 1465->1466 1467 entropy = 0.999 samples = 50 value = [24, 26] class = Popular 1465->1467 1472 entropy = 0.7 samples = 95 value = [77, 18] class = Not Popular 1471->1472 1473 entropy = 0.83 samples = 42 value = [31, 11] class = Not Popular 1471->1473 1475 Artist_Popularity ≤ 0.484 entropy = 0.942 samples = 117 value = [42, 75] class = Popular 1474->1475 1478 Artist_Popularity ≤ 0.452 entropy = 0.989 samples = 1131 value = [636, 495] class = Not Popular 1474->1478 1476 entropy = 0.953 samples = 59 value = [22, 37] class = Popular 1475->1476 1477 entropy = 0.929 samples = 58 value = [20, 38] class = Popular 1475->1477 1479 Artist_Followers ≤ 0.017 entropy = 0.949 samples = 177 value = [112, 65] class = Not Popular 1478->1479 1482 Artist_Followers ≤ 0.015 entropy = 0.993 samples = 954 value = [524, 430] class = Not Popular 1478->1482 1480 entropy = 0.99 samples = 86 value = [48, 38] class = Not Popular 1479->1480 1481 entropy = 0.877 samples = 91 value = [64, 27] class = Not Popular 1479->1481 1483 Artist_Followers ≤ 0.015 entropy = 0.972 samples = 346 value = [207, 139] class = Not Popular 1482->1483 1494 Artist_Popularity ≤ 0.5 entropy = 0.999 samples = 608 value = [317, 291] class = Not Popular 1482->1494 1484 Artist_Popularity ≤ 0.476 entropy = 1.0 samples = 269 value = [137, 132] class = Not Popular 1483->1484 1493 entropy = 0.439 samples = 77 value = [70, 7] class = Not Popular 1483->1493 1485 Artist_Followers ≤ 0.014 entropy = 0.983 samples = 111 value = [64, 47] class = Not Popular 1484->1485 1488 Artist_Followers ≤ 0.014 entropy = 0.996 samples = 158 value = [73, 85] class = Popular 1484->1488 1486 entropy = 0.958 samples = 50 value = [31, 19] class = Not Popular 1485->1486 1487 entropy = 0.995 samples = 61 value = [33, 28] class = Not Popular 1485->1487 1489 entropy = 0.997 samples = 66 value = [35, 31] class = Not Popular 1488->1489 1490 Artist_Popularity ≤ 0.5 entropy = 0.978 samples = 92 value = [38, 54] class = Popular 1488->1490 1491 entropy = 1.0 samples = 57 value = [28, 29] class = Popular 1490->1491 1492 entropy = 0.863 samples = 35 value = [10, 25] class = Popular 1490->1492 1495 Artist_Followers ≤ 0.019 entropy = 0.997 samples = 438 value = [205, 233] class = Popular 1494->1495 1514 Artist_Followers ≤ 0.016 entropy = 0.926 samples = 170 value = [112, 58] class = Not Popular 1494->1514 1496 Artist_Followers ≤ 0.018 entropy = 0.97 samples = 296 value = [118, 178] class = Popular 1495->1496 1511 Artist_Popularity ≤ 0.468 entropy = 0.963 samples = 142 value = [87, 55] class = Not Popular 1495->1511 1497 Artist_Followers ≤ 0.017 entropy = 0.987 samples = 273 value = [118, 155] class = Popular 1496->1497 1510 entropy = 0.0 samples = 23 value = [0, 23] class = Popular 1496->1510 1498 Artist_Followers ≤ 0.016 entropy = 1.0 samples = 179 value = [88, 91] class = Popular 1497->1498 1507 Artist_Followers ≤ 0.018 entropy = 0.903 samples = 94 value = [30, 64] class = Popular 1497->1507 1499 Artist_Popularity ≤ 0.468 entropy = 0.994 samples = 141 value = [64, 77] class = Popular 1498->1499 1506 entropy = 0.949 samples = 38 value = [24, 14] class = Not Popular 1498->1506 1500 entropy = 0.962 samples = 44 value = [27, 17] class = Not Popular 1499->1500 1501 Artist_Followers ≤ 0.016 entropy = 0.959 samples = 97 value = [37, 60] class = Popular 1499->1501 1502 Artist_Followers ≤ 0.016 entropy = 0.96 samples = 47 value = [18, 29] class = Popular 1501->1502 1505 entropy = 0.958 samples = 50 value = [19, 31] class = Popular 1501->1505 1503 entropy = 0.96 samples = 34 value = [13, 21] class = Popular 1502->1503 1504 entropy = 0.961 samples = 13 value = [5, 8] class = Popular 1502->1504 1508 entropy = 0.839 samples = 41 value = [11, 30] class = Popular 1507->1508 1509 entropy = 0.941 samples = 53 value = [19, 34] class = Popular 1507->1509 1512 entropy = 0.915 samples = 91 value = [61, 30] class = Not Popular 1511->1512 1513 entropy = 1.0 samples = 51 value = [26, 25] class = Not Popular 1511->1513 1515 entropy = 1.0 samples = 48 value = [24, 24] class = Not Popular 1514->1515 1516 Artist_Followers ≤ 0.019 entropy = 0.854 samples = 122 value = [88, 34] class = Not Popular 1514->1516 1517 Artist_Followers ≤ 0.018 entropy = 0.748 samples = 75 value = [59, 16] class = Not Popular 1516->1517 1520 entropy = 0.96 samples = 47 value = [29, 18] class = Not Popular 1516->1520 1518 entropy = 0.65 samples = 24 value = [20, 4] class = Not Popular 1517->1518 1519 entropy = 0.787 samples = 51 value = [39, 12] class = Not Popular 1517->1519 1522 Artist_Popularity ≤ 0.5 entropy = 0.914 samples = 143 value = [47, 96] class = Popular 1521->1522 1531 Artist_Followers ≤ 0.022 entropy = 1.0 samples = 215 value = [105, 110] class = Popular 1521->1531 1523 Artist_Popularity ≤ 0.468 entropy = 0.953 samples = 126 value = [47, 79] class = Popular 1522->1523 1530 entropy = 0.0 samples = 17 value = [0, 17] class = Popular 1522->1530 1524 Artist_Followers ≤ 0.022 entropy = 0.579 samples = 29 value = [4, 25] class = Popular 1523->1524 1527 Artist_Followers ≤ 0.021 entropy = 0.991 samples = 97 value = [43, 54] class = Popular 1523->1527 1525 entropy = 0.954 samples = 8 value = [3, 5] class = Popular 1524->1525 1526 entropy = 0.276 samples = 21 value = [1, 20] class = Popular 1524->1526 1528 entropy = 0.997 samples = 47 value = [22, 25] class = Popular 1527->1528 1529 entropy = 0.981 samples = 50 value = [21, 29] class = Popular 1527->1529 1532 Artist_Followers ≤ 0.022 entropy = 0.988 samples = 122 value = [69, 53] class = Not Popular 1531->1532 1537 Artist_Popularity ≤ 0.484 entropy = 0.963 samples = 93 value = [36, 57] class = Popular 1531->1537 1533 entropy = 0.993 samples = 51 value = [28, 23] class = Not Popular 1532->1533 1534 Artist_Popularity ≤ 0.484 entropy = 0.983 samples = 71 value = [41, 30] class = Not Popular 1532->1534 1535 entropy = 0.98 samples = 36 value = [21, 15] class = Not Popular 1534->1535 1536 entropy = 0.985 samples = 35 value = [20, 15] class = Not Popular 1534->1536 1538 entropy = 0.99 samples = 52 value = [23, 29] class = Popular 1537->1538 1539 entropy = 0.901 samples = 41 value = [13, 28] class = Popular 1537->1539 1541 Artist_Followers ≤ 0.028 entropy = 0.905 samples = 848 value = [576, 272] class = Not Popular 1540->1541 1562 Artist_Followers ≤ 0.032 entropy = 0.986 samples = 540 value = [308, 232] class = Not Popular 1540->1562 1542 Artist_Followers ≤ 0.026 entropy = 0.934 samples = 609 value = [396, 213] class = Not Popular 1541->1542 1559 Artist_Popularity ≤ 0.492 entropy = 0.806 samples = 239 value = [180, 59] class = Not Popular 1541->1559 1543 Artist_Followers ≤ 0.025 entropy = 0.861 samples = 405 value = [290, 115] class = Not Popular 1542->1543 1554 Artist_Followers ≤ 0.027 entropy = 0.999 samples = 204 value = [106, 98] class = Not Popular 1542->1554 1544 Artist_Followers ≤ 0.024 entropy = 0.969 samples = 149 value = [90, 59] class = Not Popular 1543->1544 1547 Artist_Popularity ≤ 0.484 entropy = 0.758 samples = 256 value = [200, 56] class = Not Popular 1543->1547 1545 entropy = 0.881 samples = 70 value = [49, 21] class = Not Popular 1544->1545 1546 entropy = 0.999 samples = 79 value = [41, 38] class = Not Popular 1544->1546 1548 entropy = 0.592 samples = 63 value = [54, 9] class = Not Popular 1547->1548 1549 Artist_Followers ≤ 0.026 entropy = 0.801 samples = 193 value = [146, 47] class = Not Popular 1547->1549 1550 Artist_Popularity ≤ 0.516 entropy = 0.832 samples = 110 value = [81, 29] class = Not Popular 1549->1550 1553 entropy = 0.754 samples = 83 value = [65, 18] class = Not Popular 1549->1553 1551 entropy = 0.887 samples = 46 value = [32, 14] class = Not Popular 1550->1551 1552 entropy = 0.786 samples = 64 value = [49, 15] class = Not Popular 1550->1552 1555 entropy = 0.977 samples = 78 value = [46, 32] class = Not Popular 1554->1555 1556 Artist_Followers ≤ 0.028 entropy = 0.998 samples = 126 value = [60, 66] class = Popular 1554->1556 1557 entropy = 0.995 samples = 50 value = [23, 27] class = Popular 1556->1557 1558 entropy = 1.0 samples = 76 value = [37, 39] class = Popular 1556->1558 1560 entropy = 0.79 samples = 135 value = [103, 32] class = Not Popular 1559->1560 1561 entropy = 0.826 samples = 104 value = [77, 27] class = Not Popular 1559->1561 1563 Artist_Followers ≤ 0.031 entropy = 0.974 samples = 239 value = [97, 142] class = Popular 1562->1563 1570 Artist_Popularity ≤ 0.5 entropy = 0.88 samples = 301 value = [211, 90] class = Not Popular 1562->1570 1564 Artist_Followers ≤ 0.03 entropy = 1.0 samples = 113 value = [56, 57] class = Popular 1563->1564 1567 Artist_Popularity ≤ 0.5 entropy = 0.91 samples = 126 value = [41, 85] class = Popular 1563->1567 1565 entropy = 0.82 samples = 43 value = [11, 32] class = Popular 1564->1565 1566 entropy = 0.94 samples = 70 value = [45, 25] class = Not Popular 1564->1566 1568 entropy = 1.0 samples = 69 value = [34, 35] class = Popular 1567->1568 1569 entropy = 0.537 samples = 57 value = [7, 50] class = Popular 1567->1569 1571 Artist_Followers ≤ 0.038 entropy = 0.981 samples = 177 value = [103, 74] class = Not Popular 1570->1571 1576 entropy = 0.555 samples = 124 value = [108, 16] class = Not Popular 1570->1576 1572 entropy = 0.99 samples = 25 value = [11, 14] class = Popular 1571->1572 1573 Artist_Popularity ≤ 0.468 entropy = 0.968 samples = 152 value = [92, 60] class = Not Popular 1571->1573 1574 entropy = 0.974 samples = 79 value = [47, 32] class = Not Popular 1573->1574 1575 entropy = 0.961 samples = 73 value = [45, 28] class = Not Popular 1573->1575 1578 Artist_Followers ≤ 0.047 entropy = 1.0 samples = 160 value = [82, 78] class = Not Popular 1577->1578 1585 Artist_Followers ≤ 0.043 entropy = 0.801 samples = 82 value = [20, 62] class = Popular 1577->1585 1579 Artist_Followers ≤ 0.045 entropy = 0.931 samples = 78 value = [51, 27] class = Not Popular 1578->1579 1582 Artist_Followers ≤ 0.053 entropy = 0.957 samples = 82 value = [31, 51] class = Popular 1578->1582 1580 entropy = 0.998 samples = 40 value = [19, 21] class = Popular 1579->1580 1581 entropy = 0.629 samples = 38 value = [32, 6] class = Not Popular 1579->1581 1583 entropy = 0.946 samples = 44 value = [16, 28] class = Popular 1582->1583 1584 entropy = 0.968 samples = 38 value = [15, 23] class = Popular 1582->1584 1586 entropy = 0.946 samples = 55 value = [20, 35] class = Popular 1585->1586 1587 entropy = 0.0 samples = 27 value = [0, 27] class = Popular 1585->1587 1589 Artist_Followers ≤ 0.062 entropy = 0.715 samples = 188 value = [151, 37] class = Not Popular 1588->1589 1592 entropy = 0.869 samples = 100 value = [71, 29] class = Not Popular 1588->1592 1590 entropy = 0.858 samples = 103 value = [74, 29] class = Not Popular 1589->1590 1591 entropy = 0.45 samples = 85 value = [77, 8] class = Not Popular 1589->1591 1594 entropy = 0.999 samples = 50 value = [24, 26] class = Popular 1593->1594 1595 Artist_Popularity ≤ 0.516 entropy = 0.621 samples = 162 value = [25, 137] class = Popular 1593->1595 1596 Artist_Followers ≤ 0.087 entropy = 0.233 samples = 79 value = [3, 76] class = Popular 1595->1596 1599 Artist_Followers ≤ 0.122 entropy = 0.834 samples = 83 value = [22, 61] class = Popular 1595->1599 1597 entropy = 0.149 samples = 47 value = [1, 46] class = Popular 1596->1597 1598 entropy = 0.337 samples = 32 value = [2, 30] class = Popular 1596->1598 1600 entropy = 0.904 samples = 50 value = [16, 34] class = Popular 1599->1600 1601 entropy = 0.684 samples = 33 value = [6, 27] class = Popular 1599->1601 1603 Artist_Followers ≤ 0.015 entropy = 0.97 samples = 10318 value = [4112, 6206] class = Popular 1602->1603 1992 Artist_Popularity ≤ 0.786 entropy = 0.698 samples = 4700 value = [886, 3814] class = Popular 1602->1992 1604 Artist_Followers ≤ 0.006 entropy = 0.985 samples = 1326 value = [758, 568] class = Not Popular 1603->1604 1675 Artist_Popularity ≤ 0.706 entropy = 0.953 samples = 8992 value = [3354, 5638] class = Popular 1603->1675 1605 Artist_Followers ≤ 0.006 entropy = 0.934 samples = 446 value = [290, 156] class = Not Popular 1604->1605 1632 Artist_Followers ≤ 0.013 entropy = 0.997 samples = 880 value = [468, 412] class = Not Popular 1604->1632 1606 Artist_Popularity ≤ 0.659 entropy = 0.989 samples = 208 value = [117, 91] class = Not Popular 1605->1606 1621 Artist_Popularity ≤ 0.548 entropy = 0.846 samples = 238 value = [173, 65] class = Not Popular 1605->1621 1607 Artist_Followers ≤ 0.005 entropy = 0.972 samples = 194 value = [116, 78] class = Not Popular 1606->1607 1620 entropy = 0.371 samples = 14 value = [1, 13] class = Popular 1606->1620 1608 Artist_Followers ≤ 0.003 entropy = 0.94 samples = 140 value = [90, 50] class = Not Popular 1607->1608 1615 Artist_Followers ≤ 0.005 entropy = 0.999 samples = 54 value = [26, 28] class = Popular 1607->1615 1609 entropy = 0.996 samples = 13 value = [6, 7] class = Popular 1608->1609 1610 Artist_Followers ≤ 0.004 entropy = 0.923 samples = 127 value = [84, 43] class = Not Popular 1608->1610 1611 entropy = 0.868 samples = 38 value = [27, 11] class = Not Popular 1610->1611 1612 Artist_Followers ≤ 0.005 entropy = 0.942 samples = 89 value = [57, 32] class = Not Popular 1610->1612 1613 entropy = 0.976 samples = 49 value = [29, 20] class = Not Popular 1612->1613 1614 entropy = 0.881 samples = 40 value = [28, 12] class = Not Popular 1612->1614 1616 entropy = 0.0 samples = 13 value = [0, 13] class = Popular 1615->1616 1617 Artist_Followers ≤ 0.006 entropy = 0.947 samples = 41 value = [26, 15] class = Not Popular 1615->1617 1618 entropy = 0.904 samples = 25 value = [17, 8] class = Not Popular 1617->1618 1619 entropy = 0.989 samples = 16 value = [9, 7] class = Not Popular 1617->1619 1622 Artist_Followers ≤ 0.006 entropy = 0.722 samples = 80 value = [64, 16] class = Not Popular 1621->1622 1625 Artist_Popularity ≤ 0.571 entropy = 0.893 samples = 158 value = [109, 49] class = Not Popular 1621->1625 1623 entropy = 0.708 samples = 57 value = [46, 11] class = Not Popular 1622->1623 1624 entropy = 0.755 samples = 23 value = [18, 5] class = Not Popular 1622->1624 1626 entropy = 0.985 samples = 42 value = [24, 18] class = Not Popular 1625->1626 1627 Artist_Followers ≤ 0.006 entropy = 0.837 samples = 116 value = [85, 31] class = Not Popular 1625->1627 1628 entropy = 0.963 samples = 31 value = [19, 12] class = Not Popular 1627->1628 1629 Artist_Followers ≤ 0.006 entropy = 0.767 samples = 85 value = [66, 19] class = Not Popular 1627->1629 1630 entropy = 0.669 samples = 40 value = [33, 7] class = Not Popular 1629->1630 1631 entropy = 0.837 samples = 45 value = [33, 12] class = Not Popular 1629->1631 1633 Artist_Followers ≤ 0.008 entropy = 0.986 samples = 473 value = [204, 269] class = Popular 1632->1633 1660 Artist_Followers ≤ 0.015 entropy = 0.935 samples = 407 value = [264, 143] class = Not Popular 1632->1660 1634 Artist_Followers ≤ 0.007 entropy = 0.982 samples = 147 value = [85, 62] class = Not Popular 1633->1634 1641 Artist_Followers ≤ 0.01 entropy = 0.947 samples = 326 value = [119, 207] class = Popular 1633->1641 1635 Artist_Followers ≤ 0.007 entropy = 0.977 samples = 68 value = [28, 40] class = Popular 1634->1635 1638 Artist_Followers ≤ 0.007 entropy = 0.853 samples = 79 value = [57, 22] class = Not Popular 1634->1638 1636 entropy = 0.968 samples = 38 value = [15, 23] class = Popular 1635->1636 1637 entropy = 0.987 samples = 30 value = [13, 17] class = Popular 1635->1637 1639 entropy = 0.876 samples = 44 value = [31, 13] class = Not Popular 1638->1639 1640 entropy = 0.822 samples = 35 value = [26, 9] class = Not Popular 1638->1640 1642 Artist_Followers ≤ 0.009 entropy = 0.684 samples = 132 value = [24, 108] class = Popular 1641->1642 1649 Artist_Followers ≤ 0.011 entropy = 1.0 samples = 194 value = [95, 99] class = Popular 1641->1649 1643 Artist_Popularity ≤ 0.563 entropy = 0.783 samples = 103 value = [24, 79] class = Popular 1642->1643 1648 entropy = 0.0 samples = 29 value = [0, 29] class = Popular 1642->1648 1644 entropy = 0.902 samples = 44 value = [14, 30] class = Popular 1643->1644 1645 Artist_Popularity ≤ 0.627 entropy = 0.657 samples = 59 value = [10, 49] class = Popular 1643->1645 1646 entropy = 0.0 samples = 30 value = [0, 30] class = Popular 1645->1646 1647 entropy = 0.929 samples = 29 value = [10, 19] class = Popular 1645->1647 1650 entropy = 0.675 samples = 45 value = [37, 8] class = Not Popular 1649->1650 1651 Artist_Followers ≤ 0.011 entropy = 0.964 samples = 149 value = [58, 91] class = Popular 1649->1651 1652 Artist_Followers ≤ 0.011 entropy = 1.0 samples = 77 value = [39, 38] class = Not Popular 1651->1652 1655 Artist_Popularity ≤ 0.627 entropy = 0.833 samples = 72 value = [19, 53] class = Popular 1651->1655 1653 entropy = 0.995 samples = 35 value = [16, 19] class = Popular 1652->1653 1654 entropy = 0.993 samples = 42 value = [23, 19] class = Not Popular 1652->1654 1656 Artist_Followers ≤ 0.012 entropy = 0.881 samples = 60 value = [18, 42] class = Popular 1655->1656 1659 entropy = 0.414 samples = 12 value = [1, 11] class = Popular 1655->1659 1657 entropy = 0.797 samples = 29 value = [7, 22] class = Popular 1656->1657 1658 entropy = 0.938 samples = 31 value = [11, 20] class = Popular 1656->1658 1661 Artist_Followers ≤ 0.013 entropy = 0.963 samples = 281 value = [172, 109] class = Not Popular 1660->1661 1674 entropy = 0.841 samples = 126 value = [92, 34] class = Not Popular 1660->1674 1662 Artist_Followers ≤ 0.013 entropy = 0.901 samples = 104 value = [71, 33] class = Not Popular 1661->1662 1665 Artist_Popularity ≤ 0.563 entropy = 0.986 samples = 177 value = [101, 76] class = Not Popular 1661->1665 1663 entropy = 0.93 samples = 55 value = [36, 19] class = Not Popular 1662->1663 1664 entropy = 0.863 samples = 49 value = [35, 14] class = Not Popular 1662->1664 1666 Artist_Followers ≤ 0.014 entropy = 0.997 samples = 111 value = [59, 52] class = Not Popular 1665->1666 1671 Artist_Popularity ≤ 0.587 entropy = 0.946 samples = 66 value = [42, 24] class = Not Popular 1665->1671 1667 entropy = 0.974 samples = 32 value = [19, 13] class = Not Popular 1666->1667 1668 Artist_Followers ≤ 0.015 entropy = 1.0 samples = 79 value = [40, 39] class = Not Popular 1666->1668 1669 entropy = 0.994 samples = 44 value = [20, 24] class = Popular 1668->1669 1670 entropy = 0.985 samples = 35 value = [20, 15] class = Not Popular 1668->1670 1672 entropy = 0.928 samples = 32 value = [21, 11] class = Not Popular 1671->1672 1673 entropy = 0.96 samples = 34 value = [21, 13] class = Not Popular 1671->1673 1676 Artist_Followers ≤ 0.026 entropy = 0.969 samples = 7155 value = [2834, 4321] class = Popular 1675->1676 1923 Artist_Followers ≤ 0.04 entropy = 0.86 samples = 1837 value = [520, 1317] class = Popular 1675->1923 1677 Artist_Popularity ≤ 0.683 entropy = 0.91 samples = 1247 value = [405, 842] class = Popular 1676->1677 1730 Artist_Followers ≤ 0.028 entropy = 0.977 samples = 5908 value = [2429, 3479] class = Popular 1676->1730 1678 Artist_Followers ≤ 0.024 entropy = 0.923 samples = 1197 value = [405, 792] class = Popular 1677->1678 1729 entropy = 0.0 samples = 50 value = [0, 50] class = Popular 1677->1729 1679 Artist_Popularity ≤ 0.548 entropy = 0.957 samples = 901 value = [341, 560] class = Popular 1678->1679 1720 Artist_Popularity ≤ 0.659 entropy = 0.753 samples = 296 value = [64, 232] class = Popular 1678->1720 1680 Artist_Followers ≤ 0.022 entropy = 0.825 samples = 143 value = [37, 106] class = Popular 1679->1680 1687 Artist_Followers ≤ 0.021 entropy = 0.972 samples = 758 value = [304, 454] class = Popular 1679->1687 1681 Artist_Followers ≤ 0.021 entropy = 0.93 samples = 81 value = [28, 53] class = Popular 1680->1681 1684 Artist_Followers ≤ 0.023 entropy = 0.598 samples = 62 value = [9, 53] class = Popular 1680->1684 1682 entropy = 0.961 samples = 39 value = [15, 24] class = Popular 1681->1682 1683 entropy = 0.893 samples = 42 value = [13, 29] class = Popular 1681->1683 1685 entropy = 0.0 samples = 21 value = [0, 21] class = Popular 1684->1685 1686 entropy = 0.759 samples = 41 value = [9, 32] class = Popular 1684->1686 1688 Artist_Followers ≤ 0.017 entropy = 0.926 samples = 428 value = [146, 282] class = Popular 1687->1688 1707 Artist_Followers ≤ 0.022 entropy = 0.999 samples = 330 value = [158, 172] class = Popular 1687->1707 1689 Artist_Popularity ≤ 0.587 entropy = 0.984 samples = 190 value = [81, 109] class = Popular 1688->1689 1696 Artist_Followers ≤ 0.017 entropy = 0.846 samples = 238 value = [65, 173] class = Popular 1688->1696 1690 Artist_Followers ≤ 0.016 entropy = 0.998 samples = 99 value = [52, 47] class = Not Popular 1689->1690 1693 Artist_Popularity ≤ 0.635 entropy = 0.903 samples = 91 value = [29, 62] class = Popular 1689->1693 1691 entropy = 0.998 samples = 53 value = [25, 28] class = Popular 1690->1691 1692 entropy = 0.978 samples = 46 value = [27, 19] class = Not Popular 1690->1692 1694 entropy = 0.764 samples = 36 value = [8, 28] class = Popular 1693->1694 1695 entropy = 0.959 samples = 55 value = [21, 34] class = Popular 1693->1695 1697 entropy = 0.137 samples = 52 value = [1, 51] class = Popular 1696->1697 1698 Artist_Popularity ≤ 0.643 entropy = 0.929 samples = 186 value = [64, 122] class = Popular 1696->1698 1699 Artist_Popularity ≤ 0.611 entropy = 0.964 samples = 152 value = [59, 93] class = Popular 1698->1699 1706 entropy = 0.602 samples = 34 value = [5, 29] class = Popular 1698->1706 1700 Artist_Followers ≤ 0.019 entropy = 0.915 samples = 94 value = [31, 63] class = Popular 1699->1700 1705 entropy = 0.999 samples = 58 value = [28, 30] class = Popular 1699->1705 1701 entropy = 0.946 samples = 44 value = [16, 28] class = Popular 1700->1701 1702 Artist_Followers ≤ 0.02 entropy = 0.881 samples = 50 value = [15, 35] class = Popular 1700->1702 1703 entropy = 0.0 samples = 4 value = [0, 4] class = Popular 1702->1703 1704 entropy = 0.911 samples = 46 value = [15, 31] class = Popular 1702->1704 1708 Artist_Popularity ≤ 0.643 entropy = 0.936 samples = 91 value = [59, 32] class = Not Popular 1707->1708 1711 Artist_Popularity ≤ 0.587 entropy = 0.979 samples = 239 value = [99, 140] class = Popular 1707->1711 1709 entropy = 0.949 samples = 49 value = [31, 18] class = Not Popular 1708->1709 1710 entropy = 0.918 samples = 42 value = [28, 14] class = Not Popular 1708->1710 1712 Artist_Followers ≤ 0.024 entropy = 1.0 samples = 163 value = [83, 80] class = Not Popular 1711->1712 1717 Artist_Followers ≤ 0.023 entropy = 0.742 samples = 76 value = [16, 60] class = Popular 1711->1717 1713 Artist_Followers ≤ 0.023 entropy = 1.0 samples = 117 value = [57, 60] class = Popular 1712->1713 1716 entropy = 0.988 samples = 46 value = [26, 20] class = Not Popular 1712->1716 1714 entropy = 1.0 samples = 58 value = [29, 29] class = Not Popular 1713->1714 1715 entropy = 0.998 samples = 59 value = [28, 31] class = Popular 1713->1715 1718 entropy = 0.87 samples = 55 value = [16, 39] class = Popular 1717->1718 1719 entropy = 0.0 samples = 21 value = [0, 21] class = Popular 1717->1719 1721 Artist_Followers ≤ 0.024 entropy = 0.547 samples = 222 value = [28, 194] class = Popular 1720->1721 1728 entropy = 0.999 samples = 74 value = [36, 38] class = Popular 1720->1728 1722 Artist_Popularity ≤ 0.635 entropy = 0.688 samples = 109 value = [20, 89] class = Popular 1721->1722 1725 Artist_Followers ≤ 0.025 entropy = 0.369 samples = 113 value = [8, 105] class = Popular 1721->1725 1723 entropy = 0.722 samples = 75 value = [15, 60] class = Popular 1722->1723 1724 entropy = 0.602 samples = 34 value = [5, 29] class = Popular 1722->1724 1726 entropy = 0.276 samples = 42 value = [2, 40] class = Popular 1725->1726 1727 entropy = 0.418 samples = 71 value = [6, 65] class = Popular 1725->1727 1731 Artist_Popularity ≤ 0.563 entropy = 0.965 samples = 254 value = [155, 99] class = Not Popular 1730->1731 1740 Artist_Popularity ≤ 0.548 entropy = 0.972 samples = 5654 value = [2274, 3380] class = Popular 1730->1740 1732 Artist_Followers ≤ 0.027 entropy = 0.996 samples = 106 value = [49, 57] class = Popular 1731->1732 1735 Artist_Followers ≤ 0.027 entropy = 0.861 samples = 148 value = [106, 42] class = Not Popular 1731->1735 1733 entropy = 0.992 samples = 56 value = [31, 25] class = Not Popular 1732->1733 1734 entropy = 0.943 samples = 50 value = [18, 32] class = Popular 1732->1734 1736 entropy = 0.796 samples = 79 value = [60, 19] class = Not Popular 1735->1736 1737 Artist_Followers ≤ 0.027 entropy = 0.918 samples = 69 value = [46, 23] class = Not Popular 1735->1737 1738 entropy = 0.0 samples = 9 value = [0, 9] class = Popular 1737->1738 1739 entropy = 0.784 samples = 60 value = [46, 14] class = Not Popular 1737->1739 1741 Artist_Followers ≤ 0.031 entropy = 0.997 samples = 477 value = [254, 223] class = Not Popular 1740->1741 1752 Artist_Followers ≤ 0.098 entropy = 0.965 samples = 5177 value = [2020, 3157] class = Popular 1740->1752 1742 entropy = 0.513 samples = 70 value = [62, 8] class = Not Popular 1741->1742 1743 Artist_Followers ≤ 0.035 entropy = 0.998 samples = 407 value = [192, 215] class = Popular 1741->1743 1744 Artist_Followers ≤ 0.033 entropy = 0.986 samples = 237 value = [135, 102] class = Not Popular 1743->1744 1747 Artist_Followers ≤ 0.042 entropy = 0.92 samples = 170 value = [57, 113] class = Popular 1743->1747 1745 entropy = 0.969 samples = 126 value = [76, 50] class = Not Popular 1744->1745 1746 entropy = 0.997 samples = 111 value = [59, 52] class = Not Popular 1744->1746 1748 Artist_Followers ≤ 0.035 entropy = 0.295 samples = 77 value = [4, 73] class = Popular 1747->1748 1751 entropy = 0.986 samples = 93 value = [53, 40] class = Not Popular 1747->1751 1749 entropy = 0.0 samples = 25 value = [0, 25] class = Popular 1748->1749 1750 entropy = 0.391 samples = 52 value = [4, 48] class = Popular 1748->1750 1753 Artist_Followers ≤ 0.087 entropy = 0.961 samples = 5019 value = [1925, 3094] class = Popular 1752->1753 1922 entropy = 0.97 samples = 158 value = [95, 63] class = Not Popular 1752->1922 1754 Artist_Followers ≤ 0.041 entropy = 0.965 samples = 4847 value = [1894, 2953] class = Popular 1753->1754 1917 Artist_Followers ≤ 0.092 entropy = 0.681 samples = 172 value = [31, 141] class = Popular 1753->1917 1755 Artist_Followers ≤ 0.041 entropy = 0.928 samples = 1600 value = [549, 1051] class = Popular 1754->1755 1816 Artist_Followers ≤ 0.046 entropy = 0.979 samples = 3247 value = [1345, 1902] class = Popular 1754->1816 1756 Artist_Followers ≤ 0.03 entropy = 0.935 samples = 1565 value = [549, 1016] class = Popular 1755->1756 1815 entropy = 0.0 samples = 35 value = [0, 35] class = Popular 1755->1815 1757 Artist_Followers ≤ 0.028 entropy = 0.978 samples = 489 value = [202, 287] class = Popular 1756->1757 1778 Artist_Followers ≤ 0.03 entropy = 0.907 samples = 1076 value = [347, 729] class = Popular 1756->1778 1758 Artist_Popularity ≤ 0.603 entropy = 0.687 samples = 71 value = [13, 58] class = Popular 1757->1758 1763 Artist_Followers ≤ 0.028 entropy = 0.993 samples = 418 value = [189, 229] class = Popular 1757->1763 1759 entropy = 0.0 samples = 5 value = [0, 5] class = Popular 1758->1759 1760 Artist_Followers ≤ 0.028 entropy = 0.716 samples = 66 value = [13, 53] class = Popular 1758->1760 1761 entropy = 0.614 samples = 33 value = [5, 28] class = Popular 1760->1761 1762 entropy = 0.799 samples = 33 value = [8, 25] class = Popular 1760->1762 1764 Artist_Popularity ≤ 0.563 entropy = 0.989 samples = 148 value = [83, 65] class = Not Popular 1763->1764 1767 Artist_Followers ≤ 0.028 entropy = 0.966 samples = 270 value = [106, 164] class = Popular 1763->1767 1765 entropy = 0.983 samples = 97 value = [56, 41] class = Not Popular 1764->1765 1766 entropy = 0.998 samples = 51 value = [27, 24] class = Not Popular 1764->1766 1768 entropy = 0.0 samples = 41 value = [0, 41] class = Popular 1767->1768 1769 Artist_Followers ≤ 0.029 entropy = 0.996 samples = 229 value = [106, 123] class = Popular 1767->1769 1770 Artist_Popularity ≤ 0.619 entropy = 0.937 samples = 119 value = [42, 77] class = Popular 1769->1770 1775 Artist_Followers ≤ 0.03 entropy = 0.981 samples = 110 value = [64, 46] class = Not Popular 1769->1775 1771 Artist_Followers ≤ 0.029 entropy = 0.995 samples = 85 value = [39, 46] class = Popular 1770->1771 1774 entropy = 0.431 samples = 34 value = [3, 31] class = Popular 1770->1774 1772 entropy = 0.99 samples = 50 value = [28, 22] class = Not Popular 1771->1772 1773 entropy = 0.898 samples = 35 value = [11, 24] class = Popular 1771->1773 1776 entropy = 0.975 samples = 54 value = [32, 22] class = Not Popular 1775->1776 1777 entropy = 0.985 samples = 56 value = [32, 24] class = Not Popular 1775->1777 1779 Artist_Popularity ≤ 0.603 entropy = 0.473 samples = 89 value = [9, 80] class = Popular 1778->1779 1782 Artist_Popularity ≤ 0.563 entropy = 0.927 samples = 987 value = [338, 649] class = Popular 1778->1782 1780 entropy = 0.0 samples = 22 value = [0, 22] class = Popular 1779->1780 1781 entropy = 0.569 samples = 67 value = [9, 58] class = Popular 1779->1781 1783 Artist_Followers ≤ 0.033 entropy = 0.68 samples = 111 value = [20, 91] class = Popular 1782->1783 1786 Artist_Popularity ≤ 0.69 entropy = 0.945 samples = 876 value = [318, 558] class = Popular 1782->1786 1784 entropy = 0.715 samples = 61 value = [12, 49] class = Popular 1783->1784 1785 entropy = 0.634 samples = 50 value = [8, 42] class = Popular 1783->1785 1787 Artist_Followers ≤ 0.032 entropy = 0.957 samples = 824 value = [312, 512] class = Popular 1786->1787 1814 entropy = 0.516 samples = 52 value = [6, 46] class = Popular 1786->1814 1788 Artist_Popularity ≤ 0.643 entropy = 0.999 samples = 159 value = [77, 82] class = Popular 1787->1788 1793 Artist_Popularity ≤ 0.635 entropy = 0.937 samples = 665 value = [235, 430] class = Popular 1787->1793 1789 Artist_Popularity ≤ 0.595 entropy = 0.981 samples = 100 value = [58, 42] class = Not Popular 1788->1789 1792 entropy = 0.907 samples = 59 value = [19, 40] class = Popular 1788->1792 1790 entropy = 0.981 samples = 43 value = [25, 18] class = Not Popular 1789->1790 1791 entropy = 0.982 samples = 57 value = [33, 24] class = Not Popular 1789->1791 1794 Artist_Followers ≤ 0.039 entropy = 0.88 samples = 408 value = [122, 286] class = Popular 1793->1794 1807 Artist_Followers ≤ 0.037 entropy = 0.989 samples = 257 value = [113, 144] class = Popular 1793->1807 1795 Artist_Followers ≤ 0.039 entropy = 0.804 samples = 273 value = [67, 206] class = Popular 1794->1795 1804 Artist_Followers ≤ 0.041 entropy = 0.975 samples = 135 value = [55, 80] class = Popular 1794->1804 1796 Artist_Popularity ≤ 0.579 entropy = 0.84 samples = 249 value = [67, 182] class = Popular 1795->1796 1803 entropy = 0.0 samples = 24 value = [0, 24] class = Popular 1795->1803 1797 entropy = 0.909 samples = 71 value = [23, 48] class = Popular 1796->1797 1798 Artist_Popularity ≤ 0.595 entropy = 0.807 samples = 178 value = [44, 134] class = Popular 1796->1798 1799 entropy = 0.615 samples = 46 value = [7, 39] class = Popular 1798->1799 1800 Artist_Followers ≤ 0.036 entropy = 0.856 samples = 132 value = [37, 95] class = Popular 1798->1800 1801 entropy = 0.804 samples = 53 value = [13, 40] class = Popular 1800->1801 1802 entropy = 0.886 samples = 79 value = [24, 55] class = Popular 1800->1802 1805 entropy = 1.0 samples = 86 value = [44, 42] class = Not Popular 1804->1805 1806 entropy = 0.768 samples = 49 value = [11, 38] class = Popular 1804->1806 1808 Artist_Popularity ≤ 0.675 entropy = 0.973 samples = 201 value = [81, 120] class = Popular 1807->1808 1813 entropy = 0.985 samples = 56 value = [32, 24] class = Not Popular 1807->1813 1809 Artist_Followers ≤ 0.036 entropy = 0.986 samples = 139 value = [60, 79] class = Popular 1808->1809 1812 entropy = 0.924 samples = 62 value = [21, 41] class = Popular 1808->1812 1810 entropy = 0.994 samples = 77 value = [35, 42] class = Popular 1809->1810 1811 entropy = 0.973 samples = 62 value = [25, 37] class = Popular 1809->1811 1817 Artist_Followers ≤ 0.043 entropy = 0.987 samples = 609 value = [345, 264] class = Not Popular 1816->1817 1834 Artist_Followers ≤ 0.08 entropy = 0.957 samples = 2638 value = [1000, 1638] class = Popular 1816->1834 1818 Artist_Followers ≤ 0.043 entropy = 0.971 samples = 262 value = [105, 157] class = Popular 1817->1818 1825 Artist_Followers ≤ 0.044 entropy = 0.891 samples = 347 value = [240, 107] class = Not Popular 1817->1825 1819 Artist_Popularity ≤ 0.683 entropy = 0.985 samples = 222 value = [95, 127] class = Popular 1818->1819 1824 entropy = 0.811 samples = 40 value = [10, 30] class = Popular 1818->1824 1820 Artist_Popularity ≤ 0.619 entropy = 0.999 samples = 148 value = [71, 77] class = Popular 1819->1820 1823 entropy = 0.909 samples = 74 value = [24, 50] class = Popular 1819->1823 1821 entropy = 0.995 samples = 72 value = [33, 39] class = Popular 1820->1821 1822 entropy = 1.0 samples = 76 value = [38, 38] class = Not Popular 1820->1822 1826 Artist_Followers ≤ 0.044 entropy = 0.829 samples = 176 value = [130, 46] class = Not Popular 1825->1826 1829 Artist_Followers ≤ 0.045 entropy = 0.94 samples = 171 value = [110, 61] class = Not Popular 1825->1829 1827 entropy = 0.844 samples = 114 value = [83, 31] class = Not Popular 1826->1827 1828 entropy = 0.798 samples = 62 value = [47, 15] class = Not Popular 1826->1828 1830 Artist_Popularity ≤ 0.595 entropy = 0.978 samples = 116 value = [68, 48] class = Not Popular 1829->1830 1833 entropy = 0.789 samples = 55 value = [42, 13] class = Not Popular 1829->1833 1831 entropy = 1.0 samples = 47 value = [24, 23] class = Not Popular 1830->1831 1832 entropy = 0.945 samples = 69 value = [44, 25] class = Not Popular 1830->1832 1835 Artist_Followers ≤ 0.048 entropy = 0.946 samples = 2308 value = [841, 1467] class = Popular 1834->1835 1912 Artist_Followers ≤ 0.081 entropy = 0.999 samples = 330 value = [159, 171] class = Popular 1834->1912 1836 Artist_Popularity ≤ 0.659 entropy = 0.734 samples = 97 value = [20, 77] class = Popular 1835->1836 1839 Artist_Followers ≤ 0.049 entropy = 0.952 samples = 2211 value = [821, 1390] class = Popular 1835->1839 1837 entropy = 0.0 samples = 34 value = [0, 34] class = Popular 1836->1837 1838 entropy = 0.902 samples = 63 value = [20, 43] class = Popular 1836->1838 1840 Artist_Popularity ≤ 0.627 entropy = 0.984 samples = 129 value = [74, 55] class = Not Popular 1839->1840 1843 Artist_Followers ≤ 0.054 entropy = 0.942 samples = 2082 value = [747, 1335] class = Popular 1839->1843 1841 entropy = 0.946 samples = 33 value = [21, 12] class = Not Popular 1840->1841 1842 entropy = 0.992 samples = 96 value = [53, 43] class = Not Popular 1840->1842 1844 Artist_Popularity ≤ 0.627 entropy = 0.771 samples = 208 value = [47, 161] class = Popular 1843->1844 1851 Artist_Followers ≤ 0.058 entropy = 0.953 samples = 1874 value = [700, 1174] class = Popular 1843->1851 1845 Artist_Followers ≤ 0.051 entropy = 0.882 samples = 143 value = [43, 100] class = Popular 1844->1845 1848 Artist_Followers ≤ 0.052 entropy = 0.334 samples = 65 value = [4, 61] class = Popular 1844->1848 1846 entropy = 0.96 samples = 68 value = [26, 42] class = Popular 1845->1846 1847 entropy = 0.772 samples = 75 value = [17, 58] class = Popular 1845->1847 1849 entropy = 0.0 samples = 28 value = [0, 28] class = Popular 1848->1849 1850 entropy = 0.494 samples = 37 value = [4, 33] class = Popular 1848->1850 1852 Artist_Popularity ≤ 0.595 entropy = 0.999 samples = 344 value = [179, 165] class = Not Popular 1851->1852 1859 Artist_Popularity ≤ 0.659 entropy = 0.925 samples = 1530 value = [521, 1009] class = Popular 1851->1859 1853 Artist_Followers ≤ 0.056 entropy = 0.954 samples = 136 value = [51, 85] class = Popular 1852->1853 1856 Artist_Followers ≤ 0.057 entropy = 0.961 samples = 208 value = [128, 80] class = Not Popular 1852->1856 1854 entropy = 0.992 samples = 83 value = [37, 46] class = Popular 1853->1854 1855 entropy = 0.833 samples = 53 value = [14, 39] class = Popular 1853->1855 1857 entropy = 0.959 samples = 147 value = [91, 56] class = Not Popular 1856->1857 1858 entropy = 0.967 samples = 61 value = [37, 24] class = Not Popular 1856->1858 1860 Artist_Followers ≤ 0.079 entropy = 0.961 samples = 1089 value = [418, 671] class = Popular 1859->1860 1897 Artist_Followers ≤ 0.066 entropy = 0.784 samples = 441 value = [103, 338] class = Popular 1859->1897 1861 Artist_Followers ≤ 0.06 entropy = 0.967 samples = 1038 value = [409, 629] class = Popular 1860->1861 1896 entropy = 0.672 samples = 51 value = [9, 42] class = Popular 1860->1896 1862 entropy = 0.805 samples = 61 value = [15, 46] class = Popular 1861->1862 1863 Artist_Followers ≤ 0.06 entropy = 0.973 samples = 977 value = [394, 583] class = Popular 1861->1863 1864 Artist_Followers ≤ 0.06 entropy = 0.999 samples = 116 value = [56, 60] class = Popular 1863->1864 1867 Artist_Followers ≤ 0.067 entropy = 0.966 samples = 861 value = [338, 523] class = Popular 1863->1867 1865 entropy = 0.994 samples = 55 value = [25, 30] class = Popular 1864->1865 1866 entropy = 1.0 samples = 61 value = [31, 30] class = Not Popular 1864->1866 1868 Artist_Popularity ≤ 0.571 entropy = 0.919 samples = 281 value = [94, 187] class = Popular 1867->1868 1877 Artist_Followers ≤ 0.069 entropy = 0.982 samples = 580 value = [244, 336] class = Popular 1867->1877 1869 entropy = 0.752 samples = 51 value = [11, 40] class = Popular 1868->1869 1870 Artist_Followers ≤ 0.061 entropy = 0.943 samples = 230 value = [83, 147] class = Popular 1868->1870 1871 Artist_Followers ≤ 0.061 entropy = 0.895 samples = 106 value = [33, 73] class = Popular 1870->1871 1874 Artist_Followers ≤ 0.062 entropy = 0.973 samples = 124 value = [50, 74] class = Popular 1870->1874 1872 entropy = 0.954 samples = 88 value = [33, 55] class = Popular 1871->1872 1873 entropy = 0.0 samples = 18 value = [0, 18] class = Popular 1871->1873 1875 entropy = 0.993 samples = 71 value = [32, 39] class = Popular 1874->1875 1876 entropy = 0.924 samples = 53 value = [18, 35] class = Popular 1874->1876 1878 Artist_Followers ≤ 0.067 entropy = 1.0 samples = 204 value = [100, 104] class = Popular 1877->1878 1883 Artist_Followers ≤ 0.071 entropy = 0.96 samples = 376 value = [144, 232] class = Popular 1877->1883 1879 entropy = 0.995 samples = 61 value = [28, 33] class = Popular 1878->1879 1880 Artist_Popularity ≤ 0.587 entropy = 1.0 samples = 143 value = [72, 71] class = Not Popular 1878->1880 1881 entropy = 1.0 samples = 61 value = [30, 31] class = Popular 1880->1881 1882 entropy = 1.0 samples = 82 value = [42, 40] class = Not Popular 1880->1882 1884 entropy = 0.544 samples = 40 value = [5, 35] class = Popular 1883->1884 1885 Artist_Followers ≤ 0.073 entropy = 0.978 samples = 336 value = [139, 197] class = Popular 1883->1885 1886 Artist_Popularity ≤ 0.643 entropy = 1.0 samples = 137 value = [69, 68] class = Not Popular 1885->1886 1891 Artist_Followers ≤ 0.078 entropy = 0.936 samples = 199 value = [70, 129] class = Popular 1885->1891 1887 Artist_Followers ≤ 0.071 entropy = 1.0 samples = 135 value = [67, 68] class = Popular 1886->1887 1890 entropy = 0.0 samples = 2 value = [2, 0] class = Not Popular 1886->1890 1888 entropy = 0.995 samples = 63 value = [34, 29] class = Not Popular 1887->1888 1889 entropy = 0.995 samples = 72 value = [33, 39] class = Popular 1887->1889 1892 Artist_Followers ≤ 0.076 entropy = 0.957 samples = 111 value = [42, 69] class = Popular 1891->1892 1895 entropy = 0.902 samples = 88 value = [28, 60] class = Popular 1891->1895 1893 entropy = 0.918 samples = 51 value = [17, 34] class = Popular 1892->1893 1894 entropy = 0.98 samples = 60 value = [25, 35] class = Popular 1892->1894 1898 Artist_Followers ≤ 0.059 entropy = 0.859 samples = 262 value = [74, 188] class = Popular 1897->1898 1905 Artist_Followers ≤ 0.067 entropy = 0.639 samples = 179 value = [29, 150] class = Popular 1897->1905 1899 entropy = 0.575 samples = 44 value = [6, 38] class = Popular 1898->1899 1900 Artist_Popularity ≤ 0.675 entropy = 0.895 samples = 218 value = [68, 150] class = Popular 1898->1900 1901 entropy = 0.945 samples = 69 value = [25, 44] class = Popular 1900->1901 1902 Artist_Followers ≤ 0.062 entropy = 0.867 samples = 149 value = [43, 106] class = Popular 1900->1902 1903 entropy = 0.89 samples = 52 value = [16, 36] class = Popular 1902->1903 1904 entropy = 0.853 samples = 97 value = [27, 70] class = Popular 1902->1904 1906 entropy = 0.0 samples = 46 value = [0, 46] class = Popular 1905->1906 1907 Artist_Followers ≤ 0.068 entropy = 0.757 samples = 133 value = [29, 104] class = Popular 1905->1907 1908 entropy = 0.902 samples = 44 value = [14, 30] class = Popular 1907->1908 1909 Artist_Followers ≤ 0.07 entropy = 0.654 samples = 89 value = [15, 74] class = Popular 1907->1909 1910 entropy = 0.42 samples = 47 value = [4, 43] class = Popular 1909->1910 1911 entropy = 0.83 samples = 42 value = [11, 31] class = Popular 1909->1911 1913 entropy = 0.961 samples = 164 value = [101, 63] class = Not Popular 1912->1913 1914 Artist_Popularity ≤ 0.651 entropy = 0.934 samples = 166 value = [58, 108] class = Popular 1912->1914 1915 entropy = 0.902 samples = 63 value = [20, 43] class = Popular 1914->1915 1916 entropy = 0.95 samples = 103 value = [38, 65] class = Popular 1914->1916 1918 entropy = 0.135 samples = 53 value = [1, 52] class = Popular 1917->1918 1919 Artist_Popularity ≤ 0.667 entropy = 0.815 samples = 119 value = [30, 89] class = Popular 1917->1919 1920 entropy = 0.977 samples = 34 value = [14, 20] class = Popular 1919->1920 1921 entropy = 0.698 samples = 85 value = [16, 69] class = Popular 1919->1921 1924 Artist_Popularity ≤ 0.754 entropy = 0.589 samples = 275 value = [39, 236] class = Popular 1923->1924 1939 Artist_Followers ≤ 0.073 entropy = 0.891 samples = 1562 value = [481, 1081] class = Popular 1923->1939 1925 Artist_Popularity ≤ 0.722 entropy = 0.84 samples = 93 value = [25, 68] class = Popular 1924->1925 1930 Artist_Followers ≤ 0.03 entropy = 0.391 samples = 182 value = [14, 168] class = Popular 1924->1930 1926 entropy = 0.885 samples = 33 value = [10, 23] class = Popular 1925->1926 1927 Artist_Followers ≤ 0.025 entropy = 0.811 samples = 60 value = [15, 45] class = Popular 1925->1927 1928 entropy = 0.801 samples = 41 value = [10, 31] class = Popular 1927->1928 1929 entropy = 0.831 samples = 19 value = [5, 14] class = Popular 1927->1929 1931 Artist_Followers ≤ 0.027 entropy = 0.26 samples = 114 value = [5, 109] class = Popular 1930->1931 1936 Artist_Followers ≤ 0.035 entropy = 0.564 samples = 68 value = [9, 59] class = Popular 1930->1936 1932 Artist_Popularity ≤ 0.817 entropy = 0.328 samples = 83 value = [5, 78] class = Popular 1931->1932 1935 entropy = 0.0 samples = 31 value = [0, 31] class = Popular 1931->1935 1933 entropy = 0.326 samples = 67 value = [4, 63] class = Popular 1932->1933 1934 entropy = 0.337 samples = 16 value = [1, 15] class = Popular 1932->1934 1937 entropy = 0.459 samples = 31 value = [3, 28] class = Popular 1936->1937 1938 entropy = 0.639 samples = 37 value = [6, 31] class = Popular 1936->1938 1940 Artist_Followers ≤ 0.051 entropy = 0.963 samples = 815 value = [316, 499] class = Popular 1939->1940 1965 Artist_Popularity ≤ 0.77 entropy = 0.762 samples = 747 value = [165, 582] class = Popular 1939->1965 1941 Artist_Followers ≤ 0.043 entropy = 0.819 samples = 204 value = [52, 152] class = Popular 1940->1941 1950 Artist_Followers ≤ 0.055 entropy = 0.987 samples = 611 value = [264, 347] class = Popular 1940->1950 1942 entropy = 0.995 samples = 37 value = [17, 20] class = Popular 1941->1942 1943 Artist_Popularity ≤ 0.77 entropy = 0.741 samples = 167 value = [35, 132] class = Popular 1941->1943 1944 Artist_Popularity ≤ 0.73 entropy = 0.804 samples = 110 value = [27, 83] class = Popular 1943->1944 1949 entropy = 0.585 samples = 57 value = [8, 49] class = Popular 1943->1949 1945 entropy = 0.847 samples = 62 value = [17, 45] class = Popular 1944->1945 1946 Artist_Followers ≤ 0.047 entropy = 0.738 samples = 48 value = [10, 38] class = Popular 1944->1946 1947 entropy = 0.738 samples = 24 value = [5, 19] class = Popular 1946->1947 1948 entropy = 0.738 samples = 24 value = [5, 19] class = Popular 1946->1948 1951 entropy = 0.811 samples = 68 value = [51, 17] class = Not Popular 1950->1951 1952 Artist_Popularity ≤ 0.738 entropy = 0.966 samples = 543 value = [213, 330] class = Popular 1950->1952 1953 Artist_Followers ≤ 0.068 entropy = 0.706 samples = 151 value = [29, 122] class = Popular 1952->1953 1956 Artist_Followers ≤ 0.067 entropy = 0.997 samples = 392 value = [184, 208] class = Popular 1952->1956 1954 entropy = 0.627 samples = 102 value = [16, 86] class = Popular 1953->1954 1955 entropy = 0.835 samples = 49 value = [13, 36] class = Popular 1953->1955 1957 Artist_Popularity ≤ 0.794 entropy = 0.958 samples = 150 value = [57, 93] class = Popular 1956->1957 1960 Artist_Followers ≤ 0.068 entropy = 0.998 samples = 242 value = [127, 115] class = Not Popular 1956->1960 1958 entropy = 0.961 samples = 65 value = [25, 40] class = Popular 1957->1958 1959 entropy = 0.956 samples = 85 value = [32, 53] class = Popular 1957->1959 1961 entropy = 0.942 samples = 103 value = [66, 37] class = Not Popular 1960->1961 1962 Artist_Followers ≤ 0.071 entropy = 0.989 samples = 139 value = [61, 78] class = Popular 1960->1962 1963 entropy = 0.894 samples = 58 value = [18, 40] class = Popular 1962->1963 1964 entropy = 0.997 samples = 81 value = [43, 38] class = Not Popular 1962->1964 1966 Artist_Followers ≤ 0.077 entropy = 0.518 samples = 456 value = [53, 403] class = Popular 1965->1966 1985 Artist_Popularity ≤ 0.81 entropy = 0.961 samples = 291 value = [112, 179] class = Popular 1965->1985 1967 Artist_Followers ≤ 0.075 entropy = 0.652 samples = 209 value = [35, 174] class = Popular 1966->1967 1974 Artist_Followers ≤ 0.08 entropy = 0.377 samples = 247 value = [18, 229] class = Popular 1966->1974 1968 entropy = 0.0 samples = 17 value = [0, 17] class = Popular 1967->1968 1969 Artist_Followers ≤ 0.076 entropy = 0.685 samples = 192 value = [35, 157] class = Popular 1967->1969 1970 Artist_Followers ≤ 0.075 entropy = 0.641 samples = 135 value = [22, 113] class = Popular 1969->1970 1973 entropy = 0.775 samples = 57 value = [13, 44] class = Popular 1969->1973 1971 entropy = 0.643 samples = 55 value = [9, 46] class = Popular 1970->1971 1972 entropy = 0.64 samples = 80 value = [13, 67] class = Popular 1970->1972 1975 Artist_Followers ≤ 0.079 entropy = 0.433 samples = 146 value = [13, 133] class = Popular 1974->1975 1980 Artist_Followers ≤ 0.089 entropy = 0.284 samples = 101 value = [5, 96] class = Popular 1974->1980 1976 Artist_Followers ≤ 0.078 entropy = 0.365 samples = 86 value = [6, 80] class = Popular 1975->1976 1979 entropy = 0.52 samples = 60 value = [7, 53] class = Popular 1975->1979 1977 entropy = 0.469 samples = 40 value = [4, 36] class = Popular 1976->1977 1978 entropy = 0.258 samples = 46 value = [2, 44] class = Popular 1976->1978 1981 Artist_Popularity ≤ 0.754 entropy = 0.176 samples = 38 value = [1, 37] class = Popular 1980->1981 1984 entropy = 0.341 samples = 63 value = [4, 59] class = Popular 1980->1984 1982 entropy = 0.183 samples = 36 value = [1, 35] class = Popular 1981->1982 1983 entropy = 0.0 samples = 2 value = [0, 2] class = Popular 1981->1983 1986 Artist_Popularity ≤ 0.786 entropy = 0.993 samples = 244 value = [110, 134] class = Popular 1985->1986 1989 Artist_Popularity ≤ 0.849 entropy = 0.254 samples = 47 value = [2, 45] class = Popular 1985->1989 1987 entropy = 0.99 samples = 152 value = [67, 85] class = Popular 1986->1987 1988 entropy = 0.997 samples = 92 value = [43, 49] class = Popular 1986->1988 1990 entropy = 0.362 samples = 29 value = [2, 27] class = Popular 1989->1990 1991 entropy = 0.0 samples = 18 value = [0, 18] class = Popular 1989->1991 1993 Artist_Followers ≤ 0.214 entropy = 0.811 samples = 2648 value = [662, 1986] class = Popular 1992->1993 2070 Artist_Popularity ≤ 0.921 entropy = 0.497 samples = 2052 value = [224, 1828] class = Popular 1992->2070 1994 Artist_Followers ≤ 0.108 entropy = 0.837 samples = 2421 value = [646, 1775] class = Popular 1993->1994 2065 Artist_Popularity ≤ 0.563 entropy = 0.368 samples = 227 value = [16, 211] class = Popular 1993->2065 1995 Artist_Popularity ≤ 0.706 entropy = 0.47 samples = 279 value = [28, 251] class = Popular 1994->1995 2004 Artist_Followers ≤ 0.113 entropy = 0.867 samples = 2142 value = [618, 1524] class = Popular 1994->2004 1996 Artist_Popularity ≤ 0.635 entropy = 0.565 samples = 196 value = [26, 170] class = Popular 1995->1996 2001 Artist_Popularity ≤ 0.738 entropy = 0.164 samples = 83 value = [2, 81] class = Popular 1995->2001 1997 entropy = 0.512 samples = 79 value = [9, 70] class = Popular 1996->1997 1998 Artist_Followers ≤ 0.103 entropy = 0.598 samples = 117 value = [17, 100] class = Popular 1996->1998 1999 entropy = 0.569 samples = 67 value = [9, 58] class = Popular 1998->1999 2000 entropy = 0.634 samples = 50 value = [8, 42] class = Popular 1998->2000 2002 entropy = 0.25 samples = 48 value = [2, 46] class = Popular 2001->2002 2003 entropy = 0.0 samples = 35 value = [0, 35] class = Popular 2001->2003 2005 Artist_Followers ≤ 0.11 entropy = 0.962 samples = 451 value = [174, 277] class = Popular 2004->2005 2016 Artist_Followers ≤ 0.117 entropy = 0.831 samples = 1691 value = [444, 1247] class = Popular 2004->2016 2006 Artist_Popularity ≤ 0.754 entropy = 0.771 samples = 186 value = [42, 144] class = Popular 2005->2006 2011 Artist_Popularity ≤ 0.746 entropy = 1.0 samples = 265 value = [132, 133] class = Popular 2005->2011 2007 entropy = 0.301 samples = 56 value = [3, 53] class = Popular 2006->2007 2008 Artist_Followers ≤ 0.109 entropy = 0.881 samples = 130 value = [39, 91] class = Popular 2006->2008 2009 entropy = 0.741 samples = 62 value = [13, 49] class = Popular 2008->2009 2010 entropy = 0.96 samples = 68 value = [26, 42] class = Popular 2008->2010 2012 Artist_Popularity ≤ 0.683 entropy = 0.935 samples = 148 value = [52, 96] class = Popular 2011->2012 2015 entropy = 0.9 samples = 117 value = [80, 37] class = Not Popular 2011->2015 2013 entropy = 0.977 samples = 90 value = [37, 53] class = Popular 2012->2013 2014 entropy = 0.825 samples = 58 value = [15, 43] class = Popular 2012->2014 2017 Artist_Popularity ≤ 0.73 entropy = 0.648 samples = 428 value = [71, 357] class = Popular 2016->2017 2028 Artist_Popularity ≤ 0.706 entropy = 0.876 samples = 1263 value = [373, 890] class = Popular 2016->2028 2018 Artist_Followers ≤ 0.115 entropy = 0.367 samples = 199 value = [14, 185] class = Popular 2017->2018 2025 Artist_Followers ≤ 0.114 entropy = 0.81 samples = 229 value = [57, 172] class = Popular 2017->2025 2019 Artist_Popularity ≤ 0.651 entropy = 0.174 samples = 77 value = [2, 75] class = Popular 2018->2019 2022 Artist_Followers ≤ 0.116 entropy = 0.464 samples = 122 value = [12, 110] class = Popular 2018->2022 2020 entropy = 0.179 samples = 37 value = [1, 36] class = Popular 2019->2020 2021 entropy = 0.169 samples = 40 value = [1, 39] class = Popular 2019->2021 2023 entropy = 0.535 samples = 82 value = [10, 72] class = Popular 2022->2023 2024 entropy = 0.286 samples = 40 value = [2, 38] class = Popular 2022->2024 2026 entropy = 0.885 samples = 109 value = [33, 76] class = Popular 2025->2026 2027 entropy = 0.722 samples = 120 value = [24, 96] class = Popular 2025->2027 2029 Artist_Popularity ≤ 0.69 entropy = 0.934 samples = 658 value = [230, 428] class = Popular 2028->2029 2046 Artist_Popularity ≤ 0.722 entropy = 0.789 samples = 605 value = [143, 462] class = Popular 2028->2046 2030 Artist_Popularity ≤ 0.675 entropy = 0.87 samples = 532 value = [155, 377] class = Popular 2029->2030 2045 entropy = 0.974 samples = 126 value = [75, 51] class = Not Popular 2029->2045 2031 Artist_Followers ≤ 0.139 entropy = 0.925 samples = 379 value = [129, 250] class = Popular 2030->2031 2042 Artist_Followers ≤ 0.158 entropy = 0.658 samples = 153 value = [26, 127] class = Popular 2030->2042 2032 entropy = 0.994 samples = 77 value = [35, 42] class = Popular 2031->2032 2033 Artist_Followers ≤ 0.148 entropy = 0.895 samples = 302 value = [94, 208] class = Popular 2031->2033 2034 entropy = 0.544 samples = 32 value = [4, 28] class = Popular 2033->2034 2035 Artist_Popularity ≤ 0.627 entropy = 0.918 samples = 270 value = [90, 180] class = Popular 2033->2035 2036 Artist_Followers ≤ 0.16 entropy = 0.913 samples = 207 value = [68, 139] class = Popular 2035->2036 2041 entropy = 0.933 samples = 63 value = [22, 41] class = Popular 2035->2041 2037 entropy = 0.881 samples = 60 value = [18, 42] class = Popular 2036->2037 2038 Artist_Popularity ≤ 0.563 entropy = 0.925 samples = 147 value = [50, 97] class = Popular 2036->2038 2039 entropy = 0.914 samples = 88 value = [29, 59] class = Popular 2038->2039 2040 entropy = 0.939 samples = 59 value = [21, 38] class = Popular 2038->2040 2043 entropy = 0.751 samples = 79 value = [17, 62] class = Popular 2042->2043 2044 entropy = 0.534 samples = 74 value = [9, 65] class = Popular 2042->2044 2047 entropy = 0.0 samples = 34 value = [0, 34] class = Popular 2046->2047 2048 Artist_Followers ≤ 0.117 entropy = 0.812 samples = 571 value = [143, 428] class = Popular 2046->2048 2049 entropy = 0.985 samples = 63 value = [27, 36] class = Popular 2048->2049 2050 Artist_Followers ≤ 0.118 entropy = 0.775 samples = 508 value = [116, 392] class = Popular 2048->2050 2051 entropy = 0.477 samples = 78 value = [8, 70] class = Popular 2050->2051 2052 Artist_Popularity ≤ 0.738 entropy = 0.813 samples = 430 value = [108, 322] class = Popular 2050->2052 2053 entropy = 0.999 samples = 60 value = [29, 31] class = Popular 2052->2053 2054 Artist_Followers ≤ 0.201 entropy = 0.748 samples = 370 value = [79, 291] class = Popular 2052->2054 2055 Artist_Followers ≤ 0.144 entropy = 0.675 samples = 259 value = [46, 213] class = Popular 2054->2055 2064 entropy = 0.878 samples = 111 value = [33, 78] class = Popular 2054->2064 2056 Artist_Followers ≤ 0.131 entropy = 0.769 samples = 200 value = [45, 155] class = Popular 2055->2056 2061 Artist_Popularity ≤ 0.762 entropy = 0.124 samples = 59 value = [1, 58] class = Popular 2055->2061 2057 Artist_Followers ≤ 0.119 entropy = 0.843 samples = 118 value = [32, 86] class = Popular 2056->2057 2060 entropy = 0.631 samples = 82 value = [13, 69] class = Popular 2056->2060 2058 entropy = 0.834 samples = 68 value = [18, 50] class = Popular 2057->2058 2059 entropy = 0.855 samples = 50 value = [14, 36] class = Popular 2057->2059 2062 entropy = 0.0 samples = 37 value = [0, 37] class = Popular 2061->2062 2063 entropy = 0.267 samples = 22 value = [1, 21] class = Popular 2061->2063 2066 entropy = 0.775 samples = 57 value = [13, 44] class = Popular 2065->2066 2067 Artist_Popularity ≤ 0.746 entropy = 0.128 samples = 170 value = [3, 167] class = Popular 2065->2067 2068 entropy = 0.0 samples = 130 value = [0, 130] class = Popular 2067->2068 2069 entropy = 0.384 samples = 40 value = [3, 37] class = Popular 2067->2069 2071 Artist_Followers ≤ 0.172 entropy = 0.513 samples = 1949 value = [223, 1726] class = Popular 2070->2071 2128 Artist_Popularity ≤ 0.968 entropy = 0.079 samples = 103 value = [1, 102] class = Popular 2070->2128 2072 Artist_Followers ≤ 0.137 entropy = 0.398 samples = 495 value = [39, 456] class = Popular 2071->2072 2087 Artist_Popularity ≤ 0.817 entropy = 0.548 samples = 1454 value = [184, 1270] class = Popular 2071->2087 2073 Artist_Popularity ≤ 0.865 entropy = 0.492 samples = 317 value = [34, 283] class = Popular 2072->2073 2082 Artist_Followers ≤ 0.141 entropy = 0.185 samples = 178 value = [5, 173] class = Popular 2072->2082 2074 Artist_Followers ≤ 0.133 entropy = 0.388 samples = 250 value = [19, 231] class = Popular 2073->2074 2081 entropy = 0.767 samples = 67 value = [15, 52] class = Popular 2073->2081 2075 Artist_Followers ≤ 0.118 entropy = 0.279 samples = 166 value = [8, 158] class = Popular 2074->2075 2080 entropy = 0.56 samples = 84 value = [11, 73] class = Popular 2074->2080 2076 Artist_Followers ≤ 0.104 entropy = 0.402 samples = 100 value = [8, 92] class = Popular 2075->2076 2079 entropy = 0.0 samples = 66 value = [0, 66] class = Popular 2075->2079 2077 entropy = 0.491 samples = 56 value = [6, 50] class = Popular 2076->2077 2078 entropy = 0.267 samples = 44 value = [2, 42] class = Popular 2076->2078 2083 entropy = 0.0 samples = 59 value = [0, 59] class = Popular 2082->2083 2084 Artist_Popularity ≤ 0.865 entropy = 0.251 samples = 119 value = [5, 114] class = Popular 2082->2084 2085 entropy = 0.391 samples = 65 value = [5, 60] class = Popular 2084->2085 2086 entropy = 0.0 samples = 54 value = [0, 54] class = Popular 2084->2086 2088 Artist_Followers ≤ 0.261 entropy = 0.725 samples = 288 value = [58, 230] class = Popular 2087->2088 2095 Artist_Popularity ≤ 0.881 entropy = 0.494 samples = 1166 value = [126, 1040] class = Popular 2087->2095 2089 Artist_Popularity ≤ 0.802 entropy = 0.761 samples = 263 value = [58, 205] class = Popular 2088->2089 2094 entropy = 0.0 samples = 25 value = [0, 25] class = Popular 2088->2094 2090 entropy = 0.851 samples = 83 value = [23, 60] class = Popular 2089->2090 2091 Artist_Followers ≤ 0.191 entropy = 0.711 samples = 180 value = [35, 145] class = Popular 2089->2091 2092 entropy = 0.761 samples = 127 value = [28, 99] class = Popular 2091->2092 2093 entropy = 0.563 samples = 53 value = [7, 46] class = Popular 2091->2093 2096 Artist_Followers ≤ 0.48 entropy = 0.365 samples = 631 value = [44, 587] class = Popular 2095->2096 2115 Artist_Followers ≤ 0.482 entropy = 0.618 samples = 535 value = [82, 453] class = Popular 2095->2115 2097 Artist_Followers ≤ 0.335 entropy = 0.325 samples = 522 value = [31, 491] class = Popular 2096->2097 2114 entropy = 0.527 samples = 109 value = [13, 96] class = Popular 2096->2114 2098 Artist_Popularity ≤ 0.833 entropy = 0.366 samples = 443 value = [31, 412] class = Popular 2097->2098 2113 entropy = 0.0 samples = 79 value = [0, 79] class = Popular 2097->2113 2099 Artist_Followers ≤ 0.22 entropy = 0.433 samples = 270 value = [24, 246] class = Popular 2098->2099 2108 Artist_Followers ≤ 0.312 entropy = 0.244 samples = 173 value = [7, 166] class = Popular 2098->2108 2100 entropy = 0.693 samples = 43 value = [8, 35] class = Popular 2099->2100 2101 Artist_Followers ≤ 0.236 entropy = 0.368 samples = 227 value = [16, 211] class = Popular 2099->2101 2102 entropy = 0.0 samples = 23 value = [0, 23] class = Popular 2101->2102 2103 Artist_Followers ≤ 0.254 entropy = 0.397 samples = 204 value = [16, 188] class = Popular 2101->2103 2104 entropy = 0.475 samples = 108 value = [11, 97] class = Popular 2103->2104 2105 Artist_Followers ≤ 0.266 entropy = 0.295 samples = 96 value = [5, 91] class = Popular 2103->2105 2106 entropy = 0.353 samples = 45 value = [3, 42] class = Popular 2105->2106 2107 entropy = 0.239 samples = 51 value = [2, 49] class = Popular 2105->2107 2109 Artist_Popularity ≤ 0.857 entropy = 0.076 samples = 107 value = [1, 106] class = Popular 2108->2109 2112 entropy = 0.439 samples = 66 value = [6, 60] class = Popular 2108->2112 2110 entropy = 0.0 samples = 50 value = [0, 50] class = Popular 2109->2110 2111 entropy = 0.127 samples = 57 value = [1, 56] class = Popular 2109->2111 2116 Artist_Popularity ≤ 0.897 entropy = 0.652 samples = 477 value = [80, 397] class = Popular 2115->2116 2127 entropy = 0.216 samples = 58 value = [2, 56] class = Popular 2115->2127 2117 Artist_Followers ≤ 0.198 entropy = 0.585 samples = 364 value = [51, 313] class = Popular 2116->2117 2126 entropy = 0.822 samples = 113 value = [29, 84] class = Popular 2116->2126 2118 Artist_Followers ≤ 0.185 entropy = 0.697 samples = 133 value = [25, 108] class = Popular 2117->2118 2121 Artist_Followers ≤ 0.23 entropy = 0.508 samples = 231 value = [26, 205] class = Popular 2117->2121 2119 entropy = 0.659 samples = 88 value = [15, 73] class = Popular 2118->2119 2120 entropy = 0.764 samples = 45 value = [10, 35] class = Popular 2118->2120 2122 Artist_Followers ≤ 0.21 entropy = 0.245 samples = 74 value = [3, 71] class = Popular 2121->2122 2125 entropy = 0.601 samples = 157 value = [23, 134] class = Popular 2121->2125 2123 entropy = 0.318 samples = 52 value = [3, 49] class = Popular 2122->2123 2124 entropy = 0.0 samples = 22 value = [0, 22] class = Popular 2122->2124 2129 entropy = 0.206 samples = 31 value = [1, 30] class = Popular 2128->2129 2130 entropy = 0.0 samples = 72 value = [0, 72] class = Popular 2128->2130

Confusion Matrix

In [33]:
y_pred_dt = clf_dt.predict(X_test_dt)

#Confusion Matrix
conf = confusion_matrix(y_test,y_pred_dt)

plt.figure(figsize=(16, 8))
ax = sns.heatmap(conf, annot=True,cmap='Blues',fmt='g',annot_kws={"size": 13})
# labels, title and ticks
ax.set_xlabel('Predicted Labels',fontsize = 15)
ax.set_ylabel('True Labels',fontsize = 15)
ax.set_title('Confusion Matrix of Decision Tree',fontsize = 20).set_position([.5, 1.02])
ax.xaxis.set_ticklabels(['Not Popular', 'Popular'],fontsize = 12)
ax.yaxis.set_ticklabels(['Not Popular', 'Popular'],fontsize = 12)
plt.show()

ROC Curve

In [34]:
#Probabilty Calculation
y_pred_prop = clf_dt.predict_proba(X_test_dt)[:,1]

fpr_dt, tpr_dt, _ = roc_curve(y_test, y_pred_prop)
roc_auc_dt = auc(fpr_dt, tpr_dt)

plt.figure(figsize=(14, 10))
plt.plot(fpr_dt, tpr_dt, color='darkorange',
         label='ROC curve (area = %0.2f)' % roc_auc_dt)
plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate',fontsize=18,labelpad =10)
plt.ylabel('True Positive Rate',fontsize=18)
plt.title('Receiver Operating Characteristic',fontsize=22).set_position([.5, 1.02])
plt.legend(loc="lower right",fontsize=13)
plt.show()

Precision-Recall Tradeoff

In [101]:
dt_precision, dt_recall, _ = precision_recall_curve(y_test, y_pred_dt)
no_skill = len(y_test[y_test==1]) / len(y_test)

plt.figure(figsize=(14, 10))
plt.plot(dt_recall, dt_precision, color='red',
         label='Decision Tree' % roc_auc_dt)
plt.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')
plt.xlabel('Recall',fontsize=18,labelpad =10)
plt.ylabel('Precision',fontsize=18)
plt.title('Decision Tree Precision-Recall Curve',fontsize=22).set_position([.5, 1.02])
plt.show()

Results

In [35]:
train_ac_dt = clf_dt.best_score_
accuracy_dt = accuracy_score(y_test,y_pred_dt)
precision_dt = precision_score(y_test,y_pred_dt)
recall_dt = recall_score(y_test,y_pred_dt)
f1_score_dt = f1_score(y_test,y_pred_dt)
time_dt = clf_dt.refit_time_

dt_result = [train_ac_dt,accuracy_dt,precision_dt,recall_dt,f1_score_dt,roc_auc_dt,time_dt]
dt_result = np.array(dt_result).reshape(1,-1)

f_dt = pd.DataFrame(dt_result,index=['Decision Tree'],columns = res_col)
f_dt
Out[35]:
Train Accuracy Test Accuracy Precision Recall F1 Score AUC Fitting Time
Decision Tree 0.735932 0.739827 0.738708 0.659591 0.696911 0.80365 0.02593

Support Vector Machine

Selected Features with Chi-Square

In [37]:
models = SVC(random_state=rnd)
cv = cross_validate(models,X_train_s,y_train,cv = 3, n_jobs=-1, return_estimator=True)
print("Mean training accuracy: {}".format(np.mean(cv['test_score'])))
print("Test accuracy: {}".format(cv["estimator"][0].score(X_test_s,y_test)))
Mean training accuracy: 0.7014206569337703
Test accuracy: 0.6982585105451999

Feature Selecton for SVC

For feature selection, chi-square values of the columns were considered.

In [36]:
val_svc = 0
test_svc = 0

for i in range(X_train_s.shape[1]):
    cols = X_train_s.columns[0:i+1]
    models = SVC(random_state=rnd)
    cv = cross_validate(models,X_train_s.loc[:,cols],y_train,cv = 3, n_jobs=-1, return_estimator=True)
    svc_tr = np.mean(cv['test_score'])
    svc_ts = cv["estimator"][0].score(X_test.loc[:,cols],y_test)
    if svc_tr > val_svc:
        val_svc = svc_tr
        names_svc = cols.tolist()
        test_svc = svc_ts
        nof = i + 1

print("Optimum number of features: {}".format(nof))
print('Selected features are:',names_svc,"\n")
print('Mean training accuracy with {} features: {:.6f}'.format(nof, val_svc))
print("Test accuracy with {} features: {:.6f}".format(nof, test_svc))
Optimum number of features: 7
Selected features are: ['Artist_Popularity', 'Artist_Followers', 'Album_Number', 'speechiness', 'acousticness', 'energy', 'instrumentalness'] 

Mean training accuracy with 7 features: 0.701917
Test accuracy with 7 features: 0.705872

Hyperparameter Optimization

Due to the CPU capacity, I used RandomizedSearchCV to optimize Support Vector Classifier hyperparameters.

In [38]:
X_train_svc = X_train.loc[:,names_svc]
X_test_svc = X_test.loc[:,names_svc]

param_svc = {"C": [0.0001,0.001,0.01,0.1,1,10],
              "kernel": ['linear', "poly", "rbf"],
              'gamma' : ["scale", "auto"],
            "tol": [1e-5,1e-4,1e-3,1e-2,1e-1]}


svc = SVC(random_state=rnd)


clf_svc = RandomizedSearchCV(svc,  param_svc, n_iter=15, cv=3, n_jobs = -1)
clf_svc.fit(X_train_svc, y_train)

print("Tuned Support Vector Classification Parameters: {}".format(clf_svc.best_params_)) 
print("Mean of the cv scores is {:.6f}".format(clf_svc.best_score_))
print("Test Score {:.6f}".format(clf_svc.score(X_test_svc,y_test)))
print("Seconds used for refitting the best model on the train dataset: {:.6f}".format(clf_svc.refit_time_))
Tuned Support Vector Classification Parameters: {'tol': 1e-05, 'kernel': 'rbf', 'gamma': 'scale', 'C': 1}
Mean of the cv scores is 0.701887
Test Score 0.706222
Seconds used for refitting the best model on the train dataset: 24.939646

Accuracy

In [40]:
y_pred_svc = clf_svc.predict(X_test_svc)

#Confusion Matrix
conf = confusion_matrix(y_test,y_pred_svc)

plt.figure(figsize=(16, 8))
ax = sns.heatmap(conf, annot=True,cmap='Blues',fmt='g',annot_kws={"size": 13})
# labels, title and ticks
ax.set_xlabel('Predicted Labels',fontsize = 15)
ax.set_ylabel('True Labels',fontsize = 15)
ax.set_title('Confusion Matrix of SVC',fontsize = 20).set_position([.5, 1.02])
ax.xaxis.set_ticklabels(['Not Popular', 'Popular'],fontsize = 12)
ax.yaxis.set_ticklabels(['Not Popular', 'Popular'],fontsize = 12)
plt.show()

ROC Curve

In [41]:
y_score_svc = clf_svc.decision_function(X_test_svc)

fpr_svc, tpr_svc, _ = roc_curve(y_test, y_score_svc)
roc_auc_svc = auc(fpr_svc, tpr_svc)


plt.figure(figsize=(14, 10))
plt.plot(fpr_svc, tpr_svc, color='darkorange',
         label='ROC curve (area = %0.2f)' % roc_auc_svc)
plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate',fontsize=18,labelpad =10)
plt.ylabel('True Positive Rate',fontsize=18)
plt.title('Receiver Operating Characteristic',fontsize=22).set_position([.5, 1.02])
plt.legend(loc="lower right",fontsize=13)
plt.show()

Precision-Recall Tradeoff

In [118]:
svc_precision, svc_recall, _ = precision_recall_curve(y_test, y_pred_svc)
no_skill = len(y_test[y_test==1]) / len(y_test)

plt.figure(figsize=(14, 10))
plt.plot(svc_recall, svc_precision, color='red',
         label='SVC' % roc_auc_svc)
plt.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')
plt.xlabel('Recall',fontsize=18,labelpad =10)
plt.ylabel('Precision',fontsize=18)
plt.title('SVC Precision-Recall Curve',fontsize=22).set_position([.5, 1.02])
plt.show()

Result

In [42]:
train_ac_svc = clf_svc.best_score_
accuracy_svc = accuracy_score(y_test,y_pred_svc)
precision_svc = precision_score(y_test,y_pred_svc)
recall_svc = recall_score(y_test,y_pred_svc)
f1_score_svc = f1_score(y_test,y_pred_svc)
time_svc = clf_svc.refit_time_

svc_result = [train_ac_svc,accuracy_svc,precision_svc,recall_svc,f1_score_svc,roc_auc_svc,time_svc]
svc_result = np.array(svc_result).reshape(1,-1)

f_svc=pd.DataFrame(svc_result,index=['SVM Classifier'],columns = res_col)
f_svc
Out[42]:
Train Accuracy Test Accuracy Precision Recall F1 Score AUC Fitting Time
SVM Classifier 0.701887 0.706222 0.69989 0.616557 0.655586 0.766225 24.939646

Random Forests

Selected Features with Chi-Square

In [43]:
models = RandomForestClassifier(random_state = rnd)
cv = cross_validate(models,X_train_s,y_train,cv = 3, n_jobs=-1, return_estimator=True)
print("Mean training accuracy: {}".format(np.mean(cv['test_score'])))
print("Test accuracy: {}".format(cv["estimator"][0].score(X_test_s,y_test)))
Mean training accuracy: 0.7167653498998273
Test accuracy: 0.717948717948718

Feature Selection with Random Forest Ensemble

In [44]:
from sklearn.ensemble import RandomForestClassifier

selector = SelectFromModel(RandomForestClassifier()).fit(X_train, y_train)

X_rnd_train = selector.transform(X_train)
X_rnd_test = selector.transform(X_test)

print("Selected Columns:",X.loc[:,selector.get_support()].columns.tolist(),"\n")

models = RandomForestClassifier(random_state = rnd)
cv = cross_validate(models,X_rnd_train,y_train,cv = 3, n_jobs=-1, return_estimator=True)
print("Mean training accuracy: {}".format(np.mean(cv['test_score'])))
print("Test accuracy: {}".format(cv["estimator"][0].score(X_rnd_test,y_test)))
Selected Columns: ['loudness', 'tempo', 'Duration(ms)', 'Artist_Popularity', 'Artist_Followers'] 

Mean training accuracy: 0.7195950281748256
Test accuracy: 0.7200490067384265

Hyperparameter Tunning

In [45]:
param_rnd = {"min_samples_split" : [2, 50, 150, 500],
             "criterion" : ["gini","entropy"],
             "ccp_alpha": [0,1e-4,1e-3,0.01,0.1] }


ran_forest = RandomForestClassifier(random_state = rnd)

clf_rnd = GridSearchCV(ran_forest, param_rnd, cv=3, n_jobs = -1)
clf_rnd.fit(X_rnd_train, y_train)

print("Tuned Random Forest Classification Parameters: {}".format(clf_rnd.best_params_)) 
print("Mean of the cv scores is {:.6f}".format(clf_rnd.best_score_))
print("Test Score {:.6f}".format(clf_rnd.score(X_rnd_test,y_test)))
print("Seconds used for refitting the best model on the train dataset: {:.6f}".format(clf_rnd.refit_time_))
Tuned Random Forest Classification Parameters: {'ccp_alpha': 0, 'criterion': 'entropy', 'min_samples_split': 50}
Mean of the cv scores is 0.730243
Test Score 0.735626
Seconds used for refitting the best model on the train dataset: 6.286178

Accuracy

In [116]:
y_pred_rnd = clf_rnd.predict(X_rnd_test)

#Confusion Matrix
conf = confusion_matrix(y_test,y_pred_rnd)

plt.figure(figsize=(16, 8))
ax = sns.heatmap(conf, annot=True,cmap='Blues',fmt='g',annot_kws={"size": 13})
# labels, title and ticks
ax.set_xlabel('Predicted Labels',fontsize = 15)
ax.set_ylabel('True Labels',fontsize = 15)
ax.set_title('Confusion Matrix of Random Forests',fontsize = 20).set_position([.5, 1.02])
ax.xaxis.set_ticklabels(['Not Popular', 'Popular'],fontsize = 12)
ax.yaxis.set_ticklabels(['Not Popular', 'Popular'],fontsize = 12)
plt.show()

ROC Curve

In [47]:
y_pred_prop = clf_rnd.predict_proba(X_rnd_test)[:,1]

fpr_rnd, tpr_rnd, _ = roc_curve(y_test, y_pred_prop)
roc_auc_rnd = auc(fpr_rnd, tpr_rnd)

plt.figure(figsize=(14, 10))
plt.plot(fpr_rnd, tpr_rnd, color='darkorange',
         label='ROC curve (area = %0.2f)' % roc_auc_rnd)
plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate',fontsize=18,labelpad =10)
plt.ylabel('True Positive Rate',fontsize=18)
plt.title('Receiver Operating Characteristic',fontsize=22).set_position([.5, 1.02])
plt.legend(loc="lower right",fontsize=13)
plt.show()

Precision-Recall Tradeoff

In [115]:
rnd_precision, rnd_recall, _ = precision_recall_curve(y_test, y_pred_rnd)
no_skill = len(y_test[y_test==1]) / len(y_test)

plt.figure(figsize=(14, 10))
plt.plot(rnd_recall, rnd_precision, color='red',
         label='SVC' % roc_auc_rnd)
plt.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')
plt.xlabel('Recall',fontsize=18,labelpad =10)
plt.ylabel('Precision',fontsize=18)
plt.title('Random Forests Precision-Recall Curve',fontsize=22).set_position([.5, 1.02])
plt.show()

Result

In [48]:
train_ac_rnd = clf_rnd.best_score_
accuracy_rnd = accuracy_score(y_test,y_pred_rnd)
precision_rnd = precision_score(y_test,y_pred_rnd)
recall_rnd = recall_score(y_test,y_pred_rnd)
f1_score_rnd = f1_score(y_test,y_pred_rnd)
time_rnd = clf_rnd.refit_time_

rnd_result = [train_ac_rnd,accuracy_rnd,precision_rnd,recall_rnd,f1_score_rnd,roc_auc_rnd,time_rnd]
rnd_result = np.array(rnd_result).reshape(1,-1)

f_rnd = pd.DataFrame(rnd_result,index=['Random Forest'],columns = res_col)
f_rnd
Out[48]:
Train Accuracy Test Accuracy Precision Recall F1 Score AUC Fitting Time
Random Forest 0.730243 0.735626 0.726472 0.668854 0.696473 0.805411 6.286178

XGBoost

Selected Features with Chi-Square

In [49]:
from xgboost import XGBClassifier, plot_importance

models = XGBClassifier(seed=rnd)
cv = cross_validate(models,X_train_s,y_train,cv = 3, n_jobs=-1, return_estimator=True)
print("Mean training accuracy: {}".format(np.mean(cv['test_score'])))
print("Test accuracy: {}".format(cv["estimator"][0].score(X_test_s,y_test)))
Mean training accuracy: 0.7236499693718098
Test accuracy: 0.7195239345409994

Selecting Best Features for XGBoost

In [50]:
from xgboost import XGBClassifier

models = XGBClassifier(seed=rnd,n_jobs =-1)
cv = cross_validate(models,X_train,y_train,cv = 3, n_jobs=-1, return_estimator=True)
print("Mean training accuracy: {}".format(np.mean(cv['test_score'])))
print("Test accuracy: {}".format(cv["estimator"][0].score(X_test,y_test)))
Mean training accuracy: 0.7225124176174083
Test accuracy: 0.7193489104751903
In [111]:
fi_xgb = pd.DataFrame({'Feature':X_train.columns,
       'Importance':cv["estimator"][0].feature_importances_}).sort_values(by = "Importance",ascending = True).reset_index(drop = True)

fig = px.bar(fi_xgb, x='Importance', y='Feature',orientation='h',color='Importance')
fig.show()
In [52]:
xgb_t = 0
xgb_s = 0
cv_list = list()
test_list = list()

fi_xgb = fi_xgb.sort_values(by = "Importance",ascending = False)

for i in fi_xgb.index:
    cols = fi_xgb.Feature[0:i+1]
    models = XGBClassifier(seed=rnd,n_jobs = -1)
    cv = cross_validate(models, X_train.loc[:,cols] ,y_train,cv = 3, n_jobs=-1, return_estimator=True)
    tr = np.mean(cv['test_score'])
    ts = cv["estimator"][0].score(X_test.loc[:,cols],y_test)
    cv_list.append(tr)
    test_list.append(ts)
    if tr > xgb_t:
        xgb_t = tr
        names_xgb = cols.tolist()
        xgb_s = ts
        nof = i + 1
        
print("Optimum number of features: {}".format(nof))
print('Selected features are:',names_xgb,"\n")
print('Mean training accuracy with {} features: {:.6f}'.format(nof, xgb_t))
print("Test accuracy with {} features: {:.6f}".format(nof, xgb_s))
Optimum number of features: 3
Selected features are: ['Artist_Popularity', 'Album_Number', 'Artist_Followers'] 

Mean training accuracy with 3 features: 0.737098
Test accuracy with 3 features: 0.733963

Hyperparameter Tuning

In [53]:
X_train_xgb = X_train.loc[:,names_xgb]
X_test_xgb = X_test.loc[:,names_xgb]

param_xgb = {
     "eta"    : [0.01, 0.1, 0.2] ,
     "max_depth"        : [ 3, 10,50],
     "min_child_weight" : [ 1, 4, 7 ],
     "gamma"            : [ 0.0, 0.1, 0.5 ,1],
    "reg_lambda": [0.1,1,10]}

xgb = XGBClassifier(seed=rnd,n_jobs =-1)

clf_xgb = GridSearchCV(xgb, param_xgb, cv=3, n_jobs = -1)
clf_xgb.fit(X_train_xgb, y_train)

print("Tuned XGB Classification Parameters: {}".format(clf_xgb.best_params_)) 
print("Mean of the cv scores is {:.6f}".format(clf_xgb.best_score_))
print("Test Score {:.6f}".format(clf_xgb.score(X_test_xgb,y_test)))
print("Seconds used for refitting the best model on the train dataset: {:.6f}".format(clf_xgb.refit_time_))
Tuned XGB Classification Parameters: {'eta': 0.1, 'gamma': 1, 'max_depth': 50, 'min_child_weight': 1, 'reg_lambda': 10}
Mean of the cv scores is 0.738324
Test Score 0.737376
Seconds used for refitting the best model on the train dataset: 5.269920

Accuracy

In [54]:
y_pred_xgb = clf_xgb.predict(X_test_xgb)

#Confusion Matrix
conf = confusion_matrix(y_test,y_pred_xgb)

plt.figure(figsize=(16, 8))
ax = sns.heatmap(conf, annot=True,cmap='Blues',fmt='g') #annot=True to annotate cells
# labels, title and ticks
ax.set_xlabel('Predicted Labels',fontsize = 15)
ax.set_ylabel('True Labels',fontsize = 15)
ax.set_title('Confusion Matrix of XGBoost',fontsize = 20).set_position([.5, 1.02])
ax.xaxis.set_ticklabels(['Not Popular', 'Popular'],fontsize = 12)
ax.yaxis.set_ticklabels(['Not Popular', 'Popular'],fontsize = 12)
plt.show()

Precision-Recall Tradeoff

In [117]:
xgb_precision, xgb_recall, _ = precision_recall_curve(y_test, y_pred_xgb)
no_skill = len(y_test[y_test==1]) / len(y_test)

plt.figure(figsize=(14, 10))
plt.plot(xgb_recall, xgb_precision, color='red',
         label='XGB' % roc_auc_xgb)
plt.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')
plt.xlabel('Recall',fontsize=18,labelpad =10)
plt.ylabel('Precision',fontsize=18)
plt.title('XGBClassifier Precision-Recall Curve',fontsize=22).set_position([.5, 1.02])
plt.show()

Roc Curve

In [55]:
y_pred_prop = clf_xgb.predict_proba(X_test_xgb)[:,1]

fpr_xgb, tpr_xgb, _ = roc_curve(y_test, y_pred_prop)
roc_auc_xgb = auc(fpr_xgb, tpr_xgb)

plt.figure(figsize=(14, 10))
plt.plot(fpr_xgb, tpr_xgb, color='darkorange',
         label='ROC curve (area = %0.2f)' % roc_auc_xgb)
plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate',fontsize=18,labelpad =10)
plt.ylabel('True Positive Rate',fontsize=18)
plt.title('Receiver Operating Characteristic',fontsize=22).set_position([.5, 1.02])
plt.legend(loc="lower right",fontsize=13)
plt.show()

Precision-Recall Tradeoff

In [56]:
train_ac_xgb = clf_xgb.best_score_
accuracy_xgb = accuracy_score(y_test,y_pred_xgb)
precision_xgb = precision_score(y_test,y_pred_xgb)
recall_xgb = recall_score(y_test,y_pred_xgb)
f1_score_xgb = f1_score(y_test,y_pred_xgb)
time_xgb = clf_xgb.refit_time_

res_col = ['Train Accuracy','Test Accuracy','Precision','Recall','F1 Score','AUC','Fitting Time']
xgb_result = np.array([train_ac_xgb,accuracy_xgb,precision_xgb,recall_xgb,f1_score_xgb,roc_auc_xgb,time_xgb]).reshape(1,-1)

f_xgb=pd.DataFrame(xgb_result,index=['XGBoost'],columns = res_col)
f_xgb
Out[56]:
Train Accuracy Test Accuracy Precision Recall F1 Score AUC Fitting Time
XGBoost 0.738324 0.737376 0.729918 0.668082 0.697632 0.806409 5.26992

Final Discussion

In [97]:
final_results = f_dt.append([f_svc,f_rnd,f_log,f_xgb])
final_results.sort_values(by = "AUC",ascending=False)
Out[97]:
Train Accuracy Test Accuracy Precision Recall F1 Score AUC Fitting Time
XGBoost 0.738324 0.737376 0.729918 0.668082 0.697632 0.806409 5.269920
Random Forest 0.730243 0.735626 0.726472 0.668854 0.696473 0.805411 6.286178
Decision Tree 0.735932 0.739827 0.738708 0.659591 0.696911 0.803650 0.025930
SVM Classifier 0.701887 0.706222 0.699890 0.616557 0.655586 0.766225 24.939646
Logistic Regression 0.699991 0.701146 0.687939 0.624083 0.654457 0.765147 0.212121
In [66]:
plt.figure(figsize=(14, 10))
plt.plot(fpr_xgb, tpr_xgb, color='red',
         label='ROC curve XGB (area = %0.2f)' % roc_auc_xgb)
plt.plot(fpr_rnd, tpr_rnd, color='green',
         label='ROC curve Random Forest(area = %0.2f)' % roc_auc_rnd)
plt.plot(fpr_dt, tpr_dt, color='purple',
         label='ROC curve Decision Tree(area = %0.2f)' % roc_auc_dt)

plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate',fontsize=18,labelpad =10)
plt.ylabel('True Positive Rate',fontsize=18)
plt.title('Receiver Operating Characteristic',fontsize=22).set_position([.5, 1.02])
plt.legend(loc="lower right",fontsize=13)
plt.show()

We see that best performing algorithm is XGBoost classifier if we compare them by their AUC values. Besides that, Decision Tree algorithm performed very well. Sending 2 features with the highest importance into algorithm, Artist_Popularity and Artist_Followers , decreases the model complexity and increases the performance. Also, the Decision Tree model run significantly faster than other models. It seems all 3 model, XGBoost, Random Forest and Decision Tree performs well. More hyperparameter tuning can be done on these algoritms to increase performance.

2 main feature of the tracks are selected for all model as input. Artist_Popularity and Artist_Followers. Besides these, Album_Number is one the other features that affect tracks popularity. So, we can say that, sorting the songs on albums is important for popularity. The other features have less importance on the popularity, but it is differ from model to model. One of another conclusion of the project is selecting features for model is very important in Machine Learning application. Unnecessary features are highly decrease the model performance. Thus, feature selection and feature engineering plays important role in the model building.